Correlating data indicating at least one subjective user state with data indicating at least one objective occurrence associated with a user

ABSTRACT

A computationally implemented method includes, but is not limited to: acquiring subjective user state data including data indicating at least one subjective user state associated with a user; acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user; correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence; and presenting one or more results of the correlating. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC §119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)). All subject matter ofthe Related Applications and of any and all parent, grandparent,great-grandparent, etc. applications of the Related Applications isincorporated herein by reference to the extent such subject matter isnot inconsistent herewith.

RELATED APPLICATIONS

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,135, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/313,659, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 21 Nov. 2008, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/315,083, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 26 Nov. 2008, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation or continuation-in-part. Stephen G. Kunin, Benefit ofPrior-Filed Application, USPTO Official Gazette Mar. 18, 2003, availableat http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm.The present Applicant Entity (hereinafter “Applicant”) has providedabove a specific reference to the application(s) from which priority isbeing claimed as recited by statute. Applicant understands that thestatute is unambiguous in its specific reference language and does notrequire either a serial number or any characterization, such as“continuation” or “continuation-in-part,” for claiming priority to U.S.patent applications. Notwithstanding the foregoing, Applicantunderstands that the USPTO's computer programs have certain data entryrequirements, and hence Applicant is designating the present applicationas a continuation-in-part of its parent applications as set forth above,but expressly points out that such designations are not to be construedin any way as any type of commentary and/or admission as to whether ornot the present application contains any new matter in addition to thematter of its parent application(s).

All subject matter of the Related Applications and of any and allparent, grandparent, great-grandparent, etc. applications of the RelatedApplications is incorporated herein by reference to the extent suchsubject matter is not inconsistent herewith.

SUMMARY

A computationally implemented method includes, but is not limited to:acquiring subjective user state data including data indicating at leastone subjective user state associated with a user; acquiring objectiveoccurrence data including data indicating at least one objectiveoccurrence associated with the user; correlating the subjective userstate data with the objective occurrence data based, at least in part,on a determination of at least one sequential pattern associated withthe at least one subjective user state and the at least one objectiveoccurrence; and presenting one or more results of the correlating. Inaddition to the foregoing, other method aspects are described in theclaims, drawings, and text forming a part of the present disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for acquiring subjective user state data including data indicatingat least one subjective user state associated with a user; means foracquiring objective occurrence data including data indicating at leastone objective occurrence associated with the user; means for correlatingthe subjective user state data with the objective occurrence data based,at least in part, on a determination of at least one sequential patternassociated with the at least one subjective user state and the at leastone objective occurrence; and means for presenting one or more resultsof the correlating. In addition to the foregoing, other system aspectsare described in the claims, drawings, and text forming a part of thepresent disclosure.

A computationally implemented system includes, but is not limited to:circuitry for acquiring subjective user state data including dataindicating at least one subjective user state associated with a user;circuitry for acquiring objective occurrence data including dataindicating at least one objective occurrence associated with the user;circuitry for correlating the subjective user state data with theobjective occurrence data based, at least in part, on a determination ofat least one sequential pattern associated with the at least onesubjective user state and the at least one objective occurrence; andcircuitry for presenting one or more results of the correlating. Inaddition to the foregoing, other system aspects are described in theclaims, drawings, and text forming a part of the present disclosure.

A computer program product including a signal-bearing medium bearing oneor more instructions for acquiring subjective user state data includingdata indicating at least one subjective user state associated with auser; one or more instructions for acquiring objective occurrence dataincluding data indicating at least one objective occurrence associatedwith the user; one or more instructions for correlating the subjectiveuser state data with the objective occurrence data based, at least inpart, on a determination of at least one sequential pattern associatedwith the at least one subjective user state and the at least oneobjective occurrence; and one or more instructions for presenting one ormore results of the correlating. In addition to the foregoing, othercomputer program product aspects are described in the claims, drawings,and text forming a part of the present disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1 a and 1 b show a high-level block diagram of a network deviceoperating in a network environment.

FIG. 2 a shows another perspective of the subjective user state dataacquisition module 102 of the computing device 10 of FIG. 1 b.

FIG. 2 b shows another perspective of the objective occurrence dataacquisition module 104 of the computing device 10 of FIG. 1 b.

FIG. 2 c shows another perspective of the correlation module 106 of thecomputing device 10 of FIG. 1 b.

FIG. 2 d shows another perspective of the presentation module 108 of thecomputing device 10 of FIG. 1 b.

FIG. 2 e shows another perspective of the one or more applications 126of the computing device 10 of FIG. 1 b.

FIG. 3 is a high-level logic flowchart of a process.

FIG. 4 a is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 b is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 c is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 d is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 e is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 5 a is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 b is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 c is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 d is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 e is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 f is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 g is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 h is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 i is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 j is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 k is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 6 a is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 306 of FIG. 3.

FIG. 6 b is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 306 of FIG. 3.

FIG. 6 c is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 306 of FIG. 3.

FIG. 6 d is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 306 of FIG. 3.

FIG. 7 a is a high-level logic flowchart of a process depictingalternate implementations of the presentation operation 308 of FIG. 3.

FIG. 7 b is a high-level logic flowchart of a process depictingalternate implementations of the presentation operation 308 of FIG. 3.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

A recent trend that is becoming increasingly popular in thecomputing/communication field is to electronically record one'sfeelings, thoughts, and other aspects of the person's everyday life ontoan open diary. One place where such open diaries are maintained are atsocial networking sites commonly known as “blogs” where one or moreusers may report or post their thoughts and opinions on various topics,the latest news, and various other aspects of the users' everyday life.The process of reporting or posting blog entries is commonly referred toas blogging. Other social networking sites may allow users to updatetheir personal information via, for example, social network statusreports in which a user may report or post for others to view the lateststatus or other aspects of the user.

A more recent development in social networking is the introduction andexplosive growth of microblogs in which individuals or users (referredto as “microbloggers”) maintain open diaries at microblog websites(e.g., otherwise known as “twitters”) by continuously orsemi-continuously posting microblog entries. A microblog entry (e.g.,“tweet”) is typically a short text message that is usually not more than140 characters long. The microblog entries posted by a microblogger mayreport on any aspect of the microblogger's daily life.

The various things that are typically posted through microblog entriesmay be categorized into one of at least two possible categories. Thefirst category of things that may be reported through microblog entriesare “objective occurrences” associated with the microblogger. Objectiveoccurrences that are associated with a microblogger may be anycharacteristic, event, happening, or any other aspects associated withor are of interest to the microblogger that can be objectively reportedby the microblogger, a third party, or by a device. These things wouldinclude, for example, food, medicine, or nutraceutical intake of themicroblogger, certain physical characteristics of the microblogger suchas blood sugar level or blood pressure that can be objectively measured,daily activities of the microblogger observable by others or by adevice, the local weather, the stock market (which the microblogger mayhave an interest in), activities of others (e.g., spouse or boss) thatmay directly or indirectly affect the microblogger, and so forth.

A second category of things that may be reported or posted throughmicroblogging entries include “subjective user states” of themicroblogger. Subjective user states of a microblogger include anysubjective state or status associated with the microblogger that canonly be typically reported by the microblogger (e.g., generally cannotbe reported by a third party or by a device). Such states including, forexample, the subjective mental state of the microblogger (e.g., “I amfeeling happy”), the subjective physical states of the microblogger(e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “myvision is blurry”), and the subjective overall state of the microblogger(e.g., “I'm good” or “I'm well”). Note that the term “subjective overallstate” as will be used herein refers to those subjective states that donot fit neatly into the other two categories of subjective user statesdescribed above (e.g., subjective mental states and subjective physicalstates). Although microblogs are being used to provide a wealth ofpersonal information, they have only been primarily limited to their useas a means for providing commentaries and for maintaining open diaries.

In accordance with various embodiments, methods, systems, and computerprogram products are provided for, among other things, correlatingsubjective user state data (e.g., data that indicate one or moresubjective user states of a user) with objective occurrence data (e.g.,data that indicate one or more objective occurrences associated with theuser). In doing so, a causal relationship between one or more objectiveoccurrences (e.g., cause) and one or more subjective user states (e.g.,result) associated with a user (e.g., a blogger or microblogger) may bedetermined in various alternative embodiments. For example, determiningthat the last time a user ate a banana (e.g., objective occurrence), theuser felt “good” (e.g., subjective user state) or determining whenever auser eats a banana the user always or sometimes feels good. Note that anobjective occurrence does not need to occur prior to a correspondingsubjective user state but instead, may occur subsequent or concurrentlywith the incidence of the subjective user state. For example, a personmay become “gloomy” (e.g., subjective user state) whenever it is aboutto rain (e.g., objective occurrence) or a person may become gloomy while(e.g., concurrently) it is raining.

As briefly described above, a “subjective user state” is in reference toany state or status associated with a user (e.g., a blogger ormicroblogger) at any moment or interval in time that only the user cantypically indicate or describe. Such states include, for example, thesubjective mental state of the user (e.g., user is feeling sad), thesubjective physical state (e.g., physical characteristic) of the userthat only the user can typically indicate (e.g., a backache or an easingof a backache as opposed to blood pressure which can be reported by ablood pressure device and/or a third party), and the subjective overallstate of the user (e.g., user is “good”). Examples of subjective mentalstates include, for example, happiness, sadness, depression, anger,frustration, elation, fear, alertness, sleepiness, and so forth.Examples of subjective physical states include, for example, thepresence, easing, or absence of pain, blurry vision, hearing loss, upsetstomach, physical exhaustion, and so forth. Subjective overall statesmay include any subjective user states that cannot be categorized as asubjective mental state or as a subjective physical state. Examples ofoverall states of a user that may be subjective user states include, forexample, the user being good, bad, exhausted, lack of rest, wellness,and so forth.

In contrast, “objective occurrence data,” which may also be referred toas “objective context data,” may include data that indicate one or moreobjective occurrences associated with the user that occurred atparticular intervals or points in time. An objective occurrence may beany physical characteristic, event, happenings, or any other aspectassociated with or is of interest to a user that can be objectivelyreported by at least a third party or a sensor device. Note, however,that such objective occurrence data does not have to be actuallyprovided by a sensor device or by a third party, but instead, may bereported by the user himself or herself (e.g., via microblog entries).Examples of objectively reported occurrences that could be indicated bythe objective occurrence data include, for example, a user's food,medicine, or nutraceutical intake, the user's location at any givenpoint in time, the user's exercise routine, user's blood pressure, theweather at user's location, activities associated with third parties,the stock market, and so forth.

The term “correlating” as will be used herein is in reference to adetermination of one or more relationships between at least twovariables. In the following exemplary embodiments, the first variable issubjective user state data that represents at least one subjective userstate of a user and the second variable is objective occurrence datathat represents at least one objective occurrence associated with theuser. In embodiments where the subjective user state data representsmultiple subjective user states, each of the subjective user statesrepresented by the subjective user state data may be the same or similartype of subjective user state (e.g., user being happy) at differentintervals or points in time. In alternative embodiments, however,different types of subjective user state (e.g., user being happy anduser being sad) may be represented by the subjective user state data.Similarly, in embodiments where multiple objective occurrences arerepresented by the objective occurrence data, each of the objectiveoccurrences may represent the same or similar type of objectiveoccurrence (e.g., user exercising) at different intervals or points intime, or, in alternative embodiments, different types of objectiveoccurrence (e.g., user exercising and user resting).

Various techniques may be employed for correlating the subjective userstate data with the objective occurrence data. For example, in someembodiments, correlating the objective occurrence data with thesubjective user state data may be accomplished by determining asequential pattern associated with at least one subjective user stateindicated by the subjective user state data and at least one objectiveoccurrence indicated by the objective occurrence data. In otherembodiments, correlating of the objective occurrence data with thesubjective user state data may involve determining multiple sequentialpatterns associated with multiple subjective user states and multipleobjective occurrences.

As will be further described herein a sequential pattern, in someimplementations, may merely indicate or represent the temporalrelationship or relationships between at least one subjective user stateand at least one objective occurrence (e.g., whether the incidence oroccurrence of the at least one subjective user state occurred before,after, or at least partially concurrently with the incidence of the atleast one objective occurrence). In alternative implementations, and aswill be further described herein, a sequential pattern may indicate amore specific time relationship between the incidences of one or moresubjective user states and the incidences of one or more objectiveoccurrences. For example, a sequential pattern may represent thespecific pattern of events (e.g., one or more objective occurrences andone or more subjective user states) that occurs along a timeline.

The following illustrative example is provided to describe how asequential pattern associated with at least one subjective user stateand at least one objective occurrence may be determined based, at leastin part, on the temporal relationship between the incidence of the atleast one subjective user state and the incidence of the at least oneobjective occurrence in accordance with some embodiments. For theseembodiments, the determination of a sequential pattern may initiallyinvolve determining whether the incidence of the at least one subjectiveuser state occurred within some predefined time increments of theincidence of the one objective occurrence. That is, it may be possibleto infer that those subjective user states that did not occur within acertain time period from the incidence of an objective occurrence arenot related or are unlikely related to the incidence of that objectiveoccurrence.

For example, suppose a user during the course of a day eats a banana andalso has a stomach ache sometime during the course of the day. If theconsumption of the banana occurred in the early morning hours but thestomach ache did not occur until late that night, then the stomach achemay be unrelated to the consumption of the banana and may bedisregarded. On the other hand, if the stomach ache had occurred withinsome predefined time increment, such as within 2 hours of consumption ofthe banana, then it may be concluded that there is a correlation or linkbetween the stomach ache and the consumption of the banana. If so, atemporal relationship between the consumption of the banana and theoccurrence of the stomach ache may be determined. Such a temporalrelationship may be represented by a sequential pattern. Such asequential pattern may simply indicate that the stomach ache (e.g., asubjective user state) occurred after (rather than before orconcurrently) the consumption of banana (e.g., an objective occurrence).

As will be further described herein, other factors may also bereferenced and examined in order to determine a sequential pattern andwhether there is a relationship (e.g., causal relationship) between anobjective occurrence and a subjective user state. These factors mayinclude, for example, historical data (e.g., historical medical datasuch as genetic data or past history of the user or historical datarelated to the general population regarding stomach aches and bananas).Alternatively, a sequential pattern may be determined for multiplesubjective user states and multiple objective occurrences. Such asequential pattern may particularly map the exact temporal or timesequencing of the various events (e.g., subjective user states and/orobjective occurrences). The determined sequential pattern may then beused to provide useful information to the user and/or third parties.

The following is another illustrative example of how subjective userstate data may be correlated with objective occurrence data bydetermining multiple sequential patterns and comparing the sequentialpatterns with each other. Suppose, for example, a user such as amicroblogger reports that the user ate a banana on a Monday. Theconsumption of the banana, in this example, is a reported firstobjective occurrence associated with the user. The user then reportsthat 15 minutes after eating the banana, the user felt very happy. Thereporting of the emotional state (e.g., felt very happy) is, in thisexample, a reported first subjective user state. Thus, the reportedincidence of the first objective occurrence (e.g., eating the banana)and the reported incidence of the first subjective user state (user feltvery happy) on Monday may be represented by a first sequential pattern.

On Tuesday, the user reports that the user ate another banana (e.g., asecond objective occurrence associated with the user). The user thenreports that 20 minutes after eating the second banana, the user feltsomewhat happy (e.g., a second subjective user state). Thus, thereported incidence of the second objective occurrence (e.g., eating thesecond banana) and the reported incidence of the second subjective userstate (user felt somewhat happy) on Tuesday may be represented by asecond sequential pattern. Note that in this example, the occurrences ofthe first subjective user state and the second subjective user state maybe indicated by subjective user state data while the occurrences of thefirst objective occurrence and the second objective occurrence may beindicated by objective occurrence data.

By comparing the first sequential pattern with the second sequentialpattern, the subjective user state data may be correlated with theobjective occurrence data. In some implementations, the comparison ofthe first sequential pattern with the second sequential pattern mayinvolve trying to match the first sequential pattern with the secondsequential pattern by examining certain attributes and/or metrics. Forexample, comparing the first subjective user state (e.g., user felt veryhappy) of the first sequential pattern with the second subjective userstate (e.g., user felt somewhat happy) of the second sequential patternto see if they at least substantially match or are contrasting (e.g.,being very happy in contrast to being slightly happy or being happy incontrast to being sad). Similarly, comparing the first objectiveoccurrence (e.g., eating a banana) of the first sequential pattern maybe compared to the second objective occurrence (e.g., eating of anotherbanana) of the second sequential pattern to determine whether they atleast substantially match or are contrasting.

A comparison may also be made to see if the extent of time difference(e.g., 15 minutes) between the first subjective user state (e.g., userbeing very happy) and the first objective occurrence (e.g., user eatinga banana) matches or are at least similar to the extent of timedifference (e.g., 20 minutes) between the second subjective user state(e.g., user being somewhat happy) and the second objective occurrence(e.g., user eating another banana). These comparisons may be made inorder to determine whether the first sequential pattern matches thesecond sequential pattern. A match or substantial match would suggest,for example, that a subjective user state (e.g., happiness) is linked toan objective occurrence (e.g., consumption of banana).

As briefly described above, the comparison of the first sequentialpattern with the second sequential pattern may include a determinationas to whether, for example, the respective subjective user states andthe respective objective occurrences of the sequential patterns arecontrasting subjective user states and/or contrasting objectiveoccurrences. For example, suppose in the above example the user hadreported that the user had eaten a whole banana on Monday and felt veryenergetic (e.g., first subjective user state) after eating the wholebanana (e.g., first objective occurrence). Suppose that the user alsoreported that on Tuesday he ate a half a banana instead of a wholebanana and only felt slightly energetic (e.g., second subjective userstate) after eating the half banana (e.g., second objective occurrence).In this scenario, the first sequential pattern (e.g., feeling veryenergetic after eating a whole banana) may be compared to the secondsequential pattern (e.g., feeling slightly energetic after eating only ahalf of a banana) to at least determine whether the first subjectiveuser state (e.g., being very energetic) and the second subjective userstate (e.g., being slightly energetic) are contrasting subjective userstates. Another determination may also be made during the comparison todetermine whether the first objective occurrence (eating a whole banana)is in contrast with the second objective occurrence (e.g., eating a halfof a banana).

In doing so, an inference may be made that eating a whole banana insteadof eating only a half of a banana makes the user happier or eating morebanana makes the user happier. Thus, the word “contrasting” as used herewith respect to subjective user states refers to subjective user statesthat are the same type of subjective user states (e.g., the subjectiveuser states being variations of a particular type of subjective userstates such as variations of subjective mental states). Thus, forexample, the first subjective user state and the second subjective userstate in the previous illustrative example are merely variations ofsubjective mental states (e.g., happiness). Similarly, the use of theword “contrasting” as used here with respect to objective occurrencesrefers to objective states that are the same type of objectiveoccurrences (e.g., consumption of food such as banana).

As those skilled in the art will recognize, a stronger correlationbetween the subjective user state data and the objective occurrence datacould be obtained if a greater number of sequential patterns (e.g., ifthere was a third sequential pattern, a fourth sequential pattern, andso forth, that indicated that the user became happy or happier wheneverthe user ate bananas) are used as a basis for the correlation. Note thatfor ease of explanation and illustration, each of the exemplarysequential patterns to be described herein will be depicted as asequential pattern of occurrence of a single subjective user state andoccurrence of a single objective occurrence. However, those skilled inthe art will recognize that a sequential pattern, as will be describedherein, may also be associated with occurrences of multiple objectiveoccurrences and/or multiple subjective user states. For example, supposethe user had reported that after eating a banana, he had gulped down acan of soda. The user then reported that he became happy but had anupset stomach. In this example, the sequential pattern associated withthis scenario will be associated with two objective occurrences (e.g.,eating a banana and drinking a can of soda) and two subjective userstates (e.g., user having an upset stomach and feeling happy).

In some embodiments, and as briefly described earlier, the sequentialpatterns derived from subjective user state data and objectiveoccurrence data may be based on temporal relationships between objectiveoccurrences and subjective user states. For example, whether asubjective user state occurred before, after, or at least partiallyconcurrently with an objective occurrence. For instance, a plurality ofsequential patterns derived from subjective user state data andobjective occurrence data may indicate that a user always has a stomachache (e.g., subjective user state) after eating a banana (e.g., firstobjective occurrence).

FIGS. 1 a and 1 b illustrate an example environment in accordance withvarious embodiments. In the illustrated environment, an exemplary system100 may include at least a computing device 10 (see FIG. 1 b) that maybe employed in order to, among other things, collect subjective userstate data 60 and objective occurrence data 70* that are associated witha user 20*, and to correlate the subjective user state data 60 with theobjective occurrence data 70*. Note that in the following, “*” indicatesa wildcard. Thus, user 20* may indicate a user 20 a or a user 20 b ofFIGS. 1 a and 1 b.

In some embodiments, the computing device 10 may be a network server inwhich case the computing device 10 may communicate with a user 20 a viaa mobile device 30 and through a wireless and/or wired network 40. Anetwork server, as will be described herein, may be in reference to anetwork server located at a single network site or located acrossmultiple network sites or a conglomeration of servers located atmultiple network sites. The mobile device 30 may be a variety ofcomputing/communication devices including, for example, a cellularphone, a personal digital assistant (PDA), a laptop, a desktop, or othertypes of computing/communication device that can communicate with thecomputing device 10. In alternative embodiments, the computing device 10may be a local computing device that communicates directly with a user20 b. For these embodiments, the computing device 10 may be any type ofhandheld device such as a cellular telephone or a PDA, or other types ofcomputing/communication devices such as a laptop computer, a desktopcomputer, and so forth. In certain embodiments, the computing device 10may be a peer-to-peer network component device. In some embodiments, thecomputing device 10 may operate via a web 2.0 construct.

In embodiments where the computing device 10 is a server, the computingdevice 10 may obtain the subjective user state data 60 indirectly from auser 20 a via a network interface 120. In alternative embodiments inwhich the computing device 10 is a local device, the subjective userstate data 60 may be directly obtained from a user 20 b via a userinterface 122. As will be further described, the computing device 10 mayacquire the objective occurrence data 70* from one or more sources.

For ease of illustration and explanation, the following systems andoperations to be described herein will be generally described in thecontext of the computing device 10 being a network server. However,those skilled in the art will recognize that these systems andoperations may also be implemented when the computing device 10 is alocal device such as a handheld device that may communicate directlywith a user 20 b.

Assuming that the computing device 10 is a server, the computing device10, in various implementations, may be configured to acquire subjectiveuser state data 60 including data indicating at least one subjectiveuser state 60 a via the mobile device 30 and through wireless and/orwired networks 40. In some implementations, the subjective user statedata 60 may further include additional data that may indicate one ormore additional subjective user states (e.g., data indicating at least asecond subjective user state 60 b). In various embodiments, the dataindicating the at least one subjective user state 60 a, as well as thedata indicating the at least second subjective user state 60 b, may bein the form of blog entries, such as microblog entries, status reports(e.g., social networking status reports), electronic messages (email,text messages, instant messages, etc.) or other types of electronicmessages or documents. The data indicating the at least one subjectiveuser state 60 a and the data indicating the at least second subjectiveuser state 60 b may, in some instances, indicate the same, contrasting,or completely different subjective user states. Examples of subjectiveuser states that may be indicated by the subjective user state data 60include, for example, subjective mental states of the user 20 a (e.g.,user 20 a is sad or angry), subjective physical states of the user 20 a(e.g., physical or physiological characteristic of the user 20 a such asthe presence or absence of a stomach ache or headache), subjectiveoverall states of the user 20 a (e.g., user is “well”), and/or othersubjective user states that only the user 20 a can typically indicate.

The computing device 10 may be further configured to acquire objectiveoccurrence data 70* from one or more sources. In various embodiments,the objective occurrence data 70* acquired by the computing device 10may include data indicative of at least one objective occurrenceassociated with the user 20 a. The objective occurrence data 70* mayadditionally include, in some embodiments, data indicative of one ormore additional objective occurrences associated with the user 20 aincluding data indicating at least a second objective occurrenceassociated with the user 20 a. In some embodiments, objective occurrencedata 70 a may be acquired from one or more third parties 50. Examples ofthird parties 50 include, for example, other users, a health careprovider, a hospital, a place of employment, a content provider, and soforth.

In some embodiments, objective occurrence data 70 b may be acquired fromone or more sensors 35 for sensing or monitoring various aspectsassociated with the user 20 a. For example, in some implementations,sensors 35 may include a global positioning system (GPS) device fordetermining the location of the user 20 a or a physical activity sensorfor measuring physical activities of the user 20 a. Examples of aphysical activity sensor include, for example, a pedometer for measuringphysical activities of the user 20 a. In certain implementations, theone or more sensors 35 may include one or more physiological sensordevices for measuring physiological characteristics of the user 20 a.Examples of physiological sensor devices include, for example, a bloodpressure monitor, a heart rate monitor, a glucometer, and so forth. Insome implementations, the one or more sensors 35 may include one or moreimage capturing devices such as a video or digital camera.

In some embodiments, objective occurrence data 70 c may be acquired fromthe user 20 a via the mobile device 30. For these embodiments, theobjective occurrence data 70 c may be in the form of blog entries (e.g.,microblog entries), status reports, or other types of electronicmessages. In various implementations, the objective occurrence data 70 cacquired from the user 20 a may indicate, for example, activities (e.g.,exercise or food or medicine intake) performed by the user 20 a, certainphysical characteristics (e.g., blood pressure or location) associatedwith the user 20 a, or other aspects associated with the user 20 a thatthe user 20 a can report objectively. In still other implementations,objective occurrence data 70 d may be acquired from a memory 140.

After acquiring the subjective user state data 60 and the objectiveoccurrence data 70*, the computing device 10 may be configured tocorrelate the acquired subjective user data 60 with the acquiredobjective occurrence data 70* by, for example, determining whether thereis a sequential relationship between the one or more subjective userstates as indicated by the acquired subjective user state data 60 andthe one or more objective occurrences indicated by the acquiredobjective occurrence data 70*.

In some embodiments, and as will be further indicated in the operationsand processes to be described herein, the computing device 10 may befurther configured to present one or more results of correlation. Invarious embodiments, the one or more correlation results 80 may bepresented to the user 20 a and/or to one or more third parties 50 invarious forms. The one or more third parties 50 may be other users 20*such as other microbloggers, a health care provider, advertisers, and/orcontent providers.

As illustrated in FIG. 1 b, computing device 10 may include one or morecomponents or sub-modules. For instance, in various implementations,computing device 10 may include a subjective user state data acquisitionmodule 102, an objective occurrence data acquisition module 104, acorrelation module 106, a presentation module 108, a network interface120, a user interface 122, one or more applications 126, and/or memory140. The functional roles of these components/modules will be describedin the processes and operations to be described herein.

FIG. 2 a illustrates particular implementations of the subjective userstate data acquisition module 102 of the computing device 10 of FIG. 1b. In brief, the subjective user state data acquisition module 102 maybe designed to, among other things, acquire subjective user state data60 including data indicating at least one subjective user state 60 a. Asfurther illustrated, the subjective user state data acquisition module102, in various embodiments, may include a subjective user state datareception module 202 for receiving the subjective user state data 60from a user 20 a via the network interface 120 (e.g., in the case wherethe computing device 10 is a network server). Alternatively, thesubjective user state data reception module 202 may receive thesubjective user state data 60 directly from a user 20 b (e.g., in thecase where the computing device 10 is a local device) via the userinterface 122.

In some implementations, the subjective user state data reception module202 may further include a user interface data reception module 204, anetwork interface data reception module 206, a text entry data receptionmodule 208, an audio entry data reception module 210, and/or an imageentry data reception module 212. In brief, and as will be furtherdescribed in the processes and operations to be described herein, theuser interface data reception module 204 may be configured to acquiresubjective user state data 60 via a user interface 122 (e.g., a displaymonitor, a keyboard, a touch screen, a mouse, a keypad, a microphone, acamera, and/or other interface devices) such as in the case where thecomputing device 10 is a local device to be used directly by a user 20b.

In contrast, the network interface data reception module 206 may beconfigured to acquire subjective user state data 60 via a networkinterface 120 (e.g., network interface card or NIC) such as in the casewhere the computing device 10 is a network server. The text entry datareception module 208 may be configured to receive data indicating atleast one subjective user state 60 a that was obtained based, at leastin part, on one or more text entries provided by a user 20*. The audioentry data reception module 210 may be configured to receive dataindicating at least one subjective user state 60 a that was obtained,based, at least in part, on one or more audio entries provided by a user20*. The image entry data reception module 212 may be configured toreceive data indicating at least one subjective user state 60 a that wasobtained based, at least in part, on one or more image entries providedby a user 20*.

In some embodiments, the subjective user state data acquisition module102 may include a subjective user state data solicitation module 214 forsoliciting subjective user state data 60 from a user 20*. The subjectiveuser state data solicitation module 214 may solicit the subjective userstate data 60 from a user 20 a via a network interface 120 (e.g., in thecase where the computing device 10 is a network server) or from a user20 b via a user interface 122 (e.g., in the case where the computingdevice 10 is a local device used directly by a user 20 b). Thesolicitation of the subjective user state data 60, in variousembodiments, may involve requesting a user 20* to select one or moresubjective user states from a list of alternative subjective user stateoptions (e.g., user 20* can choose at least one from a choice of “I'mfeeling alert,” “I'm feeling sad,” “My back is hurting,” “I have anupset stomach,” and so forth).

In some embodiments, the request to select from a list of alternativesubjective user state options may simply involve requesting the user 20*to select one subjective user state from two contrasting and oppositesubjective user state options (e.g., “I'm feeling good” or “I'm feelingbad”). The subjective user state data solicitation module 214 may beused in some circumstances in order to prompt a user 20* to provideuseful data. For instance, if a user 20* reports a first subjective userstate following the occurrence of a first objective occurrence, then thesubjective user state data solicitation module 214 may solicit from theuser 20* a second subjective user state following the occurrence of asecond objective occurrence.

In some implementations, the subjective user state data solicitationmodule 214 may further include a transmission module 216 fortransmitting to a user 20 a, a request (e.g., solicitation) for asubjective user state. The request or solicitation for the subjectiveuser state may be transmitted to the user 20 a via a network interface120 and may be in the form of an electronic message.

In some implementations, the subjective user state data solicitationmodule 214 may further include a display module 218 for displaying to auser 20 b, a request (e.g., solicitation) for a subjective user state.The request or solicitation for the subjective user state may bedisplayed to the user 20 b via a user interface 122 in the form of atext message, an audio message, or a visual message.

In various embodiments, the subjective user state data acquisitionmodule 102 may include a time data acquisition module 220 for acquiringtime and/or temporal elements associated with one or more subjectiveuser states of a user 20*. For these embodiments, the time and/ortemporal elements (e.g., time stamps, time interval indicators, and/ortemporal relationship indicators) acquired by the time data acquisitionmodule 220 may be useful for determining sequential patterns associatedwith subjective user states and objective occurrences as will be furtherdescribed herein. In some implementations, the time data acquisitionmodule 220 may include a time stamp acquisition module 222 for acquiring(e.g., either by receiving or generating) one or more time stampsassociated with one or more subjective user states. In the same ordifferent implementations, the time data acquisition module 220 mayinclude a time interval acquisition module 223 for acquiring (e.g.,either by receiving or generating) indications of one or more timeintervals associated with one or more subjective user states. In thesame or different implementations, the time data acquisition module 220may include a temporal relationship acquisition module 224 for acquiringindications of temporal relationships between subjective user states andobjective occurrence (e.g., an indication that a subjective user stateoccurred before, after, or at least partially concurrently withincidence of an objective occurrence).

Referring now to FIG. 2 b illustrating particular implementations of theobjective occurrence data acquisition module 104 of the computing device10 of FIG. 1 b. In various implementations, the objective occurrencedata acquisition module 104 may be configured to acquire (e.g., receive,solicit, and/or retrieve from a user 20*, one or more third parties 50,one or more sensors 35, and/or a memory 140) objective occurrence data70* including data indicative of one or more objective occurrencesassociated with a user 20*. In some embodiments, the objectiveoccurrence data acquisition module 104 may include an objectiveoccurrence data reception module 226 configured to receive (e.g., vianetwork interface 120 or via user interface 122) objective occurrencedata 70*.

In the same or different embodiments, the objective occurrence dataacquisition module 104 may include a time data acquisition module 228configured to acquire time and/or temporal elements associated with oneor more objective occurrences associated with a user 20*. For theseembodiments, the time and/or temporal elements (e.g., time stamps, timeintervals, and/or temporal relationships) may be useful for determiningsequential patterns associated with objective occurrences and subjectiveuser states. In some implementations, the time data acquisition module228 may include a time stamp acquisition module 230 for acquiring (e.g.,either by receiving or generating) one or more time stamps associatedwith one or more objective occurrences associated with a user 20*. Inthe same or different implementations, the time data acquisition module228 may include a time interval acquisition module 231 for acquiring(e.g., either by receiving or generating) indications of one or moretime intervals associated with one or more objective occurrencesassociated with a user 20*. In the same or different implementations,the time data acquisition module 228 may include a temporal relationshipacquisition module 232 for acquiring indications of temporalrelationships between objective occurrences and subjective user states(e.g., an indication that an objective occurrence occurred before,after, or at least partially concurrently with incidence of a subjectiveuser state).

In various embodiments, the objective occurrence data acquisition module104 may include an objective occurrence data solicitation module 234 forsoliciting objective occurrence data 70* from one or more sources (e.g.,a user 20*, one or more third parties 50, one or more sensors 35, and/orother sources). In some embodiments, the objective occurrence datasolicitation module 234 may be prompted to solicit objective occurrencedata 70* including data indicating one or more objective occurrences inresponse to a reporting of one or more subjective user states or to areporting of one or more other types of events. For example, if a user20* reports that he or she is feeling ill, the objective occurrence datasolicitation module 234 may request the user 20* to provide the user'sblood sugar level (i.e., an objective occurrence).

Turning now to FIG. 2 c illustrating particular implementations of thecorrelation module 106 of the computing device 10 of FIG. 1 b. Thecorrelation module 106 may be configured to, among other things,correlate subjective user state data 60 with objective occurrence data70* based, at least in part, on a determination of at least onesequential pattern of at least one objective occurrence and at least onesubjective user state. In various embodiments, the correlation module106 may include a sequential pattern determination module 236 configuredto determine one or more sequential patterns of one or more subjectiveuser states and one or more objective occurrences associated with a user20*.

The sequential pattern determination module 236, in variousimplementations, may include one or more sub-modules that may facilitatein the determination of one or more sequential patterns. As depicted,the one or more sub-modules that may be included in the sequentialpattern determination module 236 may include, for example, a “withinpredefined time increment determination” module 238, a temporalrelationship determination module 239, a subjective user state andobjective occurrence time difference determination module 240, and/or ahistorical data referencing module 241. In brief, the within predefinedtime increment determination module 238 may be configured to determinewhether at least one subjective user state of a user 20* occurred withina predefined time increment from an incidence of at least one objectiveoccurrence. For example, determining whether a user 20* feeling “bad”(i.e., a subjective user state) occurred within ten hours (i.e.,predefined time increment) of eating a large chocolate sundae (i.e., anobjective occurrence). Such a process may be used in order to filter outevents that are likely not related or to facilitate in determining thestrength of correlation between subjective user state data 60 andobjective occurrence data 70*.

The temporal relationship determination module 239 may be configured todetermine the temporal relationships between one or more subjective userstates and one or more objective occurrences. For example, this mayentail determining whether a particular subjective user state (e.g.,sore back) occurred before, after, or at least partially concurrentlywith incidence of an objective occurrence (e.g., sub-freezingtemperature).

The subjective user state and objective occurrence time differencedetermination module 240 may be configured to determine the extent oftime difference between the incidence of at least one subjective userstate and the incidence of at least one objective occurrence. Forexample, determining how long after taking a particular brand ofmedication (e.g., objective occurrence) did a user 20* feel “good”(e.g., subjective user state).

The historical data referencing module 241 may be configured toreference historical data 72 in order to facilitate in determiningsequential patterns. For example, in various implementations, thehistorical data 72 that may be referenced may include, for example,general population trends (e.g., people having a tendency to have ahangover after drinking or ibuprofen being more effective than aspirinfor toothaches in the general population), medical information such asgenetic, metabolome, or proteome information related to the user20*(e.g., genetic information of the user 20* indicating that the user20* is susceptible to a particular subjective user state in response tooccurrence of a particular objective occurrence), or historicalsequential patterns such as known sequential patterns of the generalpopulation or of the user 20*(e.g., people tending to have difficultysleeping within five hours after consumption of coffee). In someinstances, such historical data 72 may be useful in associating one ormore subjective user states with one or more objective occurrences.

In some embodiments, the correlation module 106 may include a sequentialpattern comparison module 242. As will be further described herein, thesequential pattern comparison module 242 may be configured to comparemultiple sequential patterns with each other to determine, for example,whether the sequential patterns at least substantially match each otheror to determine whether the sequential patterns are contrastingsequential patterns.

As depicted in FIG. 2 c, in various implementations, the sequentialpattern comparison module 242 may further include one or moresub-modules that may be employed in order to, for example, facilitate inthe comparison of different sequential patterns. For example, in variousimplementations, the sequential pattern comparison module 242 mayinclude one or more of a subjective user state equivalence determinationmodule 243, an objective occurrence equivalence determination module244, a subjective user state contrast determination module 245, anobjective occurrence contrast determination module 246, a temporalrelationship comparison module 247, and/or an extent of time differencecomparison module 248.

The subjective user state equivalence determination module 243 may beconfigured to determine whether subjective user states associated withdifferent sequential patterns are equivalent. For example, thesubjective user state equivalence determination module 243 determiningwhether a first subjective user state of a first sequential pattern isequivalent to a second subjective user state of a second sequentialpattern. For instance, suppose a user 20* reports that on Monday he hada stomach ache (e.g., first subjective user state) after eating at aparticular restaurant (e.g., a first objective occurrence), and supposefurther that the user 20* again reports having a stomach ache (e.g., asecond subjective user state) after eating at the same restaurant (e.g.,a second objective occurrence) on Tuesday, then the subjective userstate equivalence determination module 243 may be employed in order tocompare the first subjective user state (e.g., stomach ache) with thesecond subjective user state (e.g., stomach ache) to determine whetherthey are equivalent.

In contrast, the objective occurrence equivalence determination module244 may be configured to determine whether objective occurrences ofdifferent sequential patterns are equivalent. For example, the objectiveoccurrence equivalence determination module 244 determining whether afirst objective occurrence of a first sequential pattern is equivalentto a second objective occurrence of a second sequential pattern. Forinstance, for the above example the objective occurrence equivalencedetermination module 244 may compare eating at the particular restauranton Monday (e.g., first objective occurrence) with eating at the samerestaurant on Tuesday (e.g., second objective occurrence) in order todetermine whether the first objective occurrence is equivalent to thesecond objective occurrence.

In some implementations, the sequential pattern comparison module 242may include a subjective user state contrast determination module 245that may be configured to determine whether subjective user statesassociated with different sequential patterns are contrasting subjectiveuser states. For example, the subjective user state contrastdetermination module 245 may determine whether a first subjective userstate of a first sequential pattern is a contrasting subjective userstate from a second subjective user state of a second sequentialpattern. For instance, suppose a user 20* reports that he felt very“good” (e.g., first subjective user state) after jogging for an hour(e.g., first objective occurrence) on Monday, but reports that he felt“bad” (e.g., second subjective user state) when he did not exercise(e.g., second objective occurrence) on Tuesday, then the subjective userstate contrast determination module 245 may compare the first subjectiveuser state (e.g., feeling good) with the second subjective user state(e.g., feeling bad) to determine that they are contrasting subjectiveuser states.

In some implementations, the sequential pattern comparison module 242may include an objective occurrence contrast determination module 246that may be configured to determine whether objective occurrences ofdifferent sequential patterns are contrasting objective occurrences. Forexample, the objective occurrence contrast determination module 246 maydetermine whether a first objective occurrence of a first sequentialpattern is a contrasting objective occurrence from a second objectiveoccurrence of a second sequential pattern. For instance, for the aboveexample, the objective occurrence contrast determination module 246 maycompare the “jogging” on Monday (e.g., first objective occurrence) withthe “no jogging” on Tuesday (e.g., second objective occurrence) in orderto determine whether the first objective occurrence is a contrastingobjective occurrence from the second objective occurrence. Based on thecontrast determination, an inference may be made that the user 20* mayfeel better by jogging rather than by not jogging at all.

In some embodiments, the sequential pattern comparison module 242 mayinclude a temporal relationship comparison module 247 that may beconfigured to make comparisons between different temporal relationshipsof different sequential patterns. For example, the temporal relationshipcomparison module 247 may compare a first temporal relationship betweena first subjective user state and a first objective occurrence of afirst sequential pattern with a second temporal relationship between asecond subjective user state and a second objective occurrence of asecond sequential pattern in order to determine whether the firsttemporal relationship at least substantially matches the second temporalrelationship.

For example, suppose in the above example the user 20* eating at theparticular restaurant (e.g., first objective occurrence) and thesubsequent stomach ache (e.g., first subjective user state) on Mondayrepresents a first sequential pattern while the user 20* eating at thesame restaurant (e.g., second objective occurrence) and the subsequentstomach ache (e.g., second subjective user state) on Tuesday representsa second sequential pattern. In this example, the occurrence of thestomach ache after (rather than before or concurrently) eating at theparticular restaurant on Monday represents a first temporal relationshipassociated with the first sequential pattern while the occurrence of asecond stomach ache after (rather than before or concurrently) eating atthe same restaurant on Tuesday represents a second temporal relationshipassociated with the second sequential pattern. Under such circumstances,the temporal relationship comparison module 247 may compare the firsttemporal relationship to the second temporal relationship in order todetermine whether the first temporal relationship and the secondtemporal relationship at least substantially match (e.g., stomachachesin both temporal relationships occurring after eating at therestaurant). Such a match may result in the inference that a stomachache is associated with eating at the particular restaurant.

In some implementations, the sequential pattern comparison module 242may include an extent of time difference comparison module 248 that maybe configured to compare the extent of time differences betweenincidences of subjective user states and incidences of objectiveoccurrences of different sequential patterns. For example, the extent oftime difference comparison module 248 may compare the extent of timedifference between incidence of a first subjective user state andincidence of a first objective occurrence of a first sequential patternwith the extent of time difference between incidence of a secondsubjective user state and incidence of a second objective occurrence ofa second sequential pattern. In some implementations, the comparisonsmay be made in order to determine that the extent of time differences ofthe different sequential patterns at least substantially or proximatelymatch.

In some embodiments, the correlation module 106 may include a strengthof correlation determination module 250 for determining a strength ofcorrelation between subjective user state data 60 and objectiveoccurrence data 70* associated with a user 20*. In some implementations,the strength of correlation may be determined based, at least in part,on the results provided by the other sub-modules of the correlationmodule 106 (e.g., the sequential pattern determination module 236, thesequential pattern comparison module 242, and their sub-modules).

FIG. 2 d illustrates particular implementations of the presentationmodule 108 of the computing device 10 of FIG. 1 b. In variousimplementations, the presentation module 108 may be configured topresent one or more results of the correlation operations performed bythe correlation module 106. This may involve presenting the one or moreresults in different forms. For example, in some implementations thismay entail the presentation module 108 presenting to the user 20* anindication of a sequential relationship between a subjective user stateand an objective occurrence associated with the user 20* (e.g.,“whenever you eat a banana, you have a stomach ache). In alternativeimplementations, other ways of presenting the results of the correlationmay be employed. For example, in various alternative implementations, anotification may be provided to notify past tendencies or patternsassociated with a user 20*. In some implementations, a notification of apossible future outcome may be provided. In other implementations, arecommendation for a future course of action based on past patterns maybe provided. These and other ways of presenting the correlation resultswill be described in the processes and operations to be describedherein.

In various implementations, the presentation module 108 may include atransmission module 252 for transmitting one or more results of thecorrelation performed by the correlation module 106. For example, in thecase where the computing device 10 is a server, the transmission module252 may be configured to transmit to the user 20 a or a third party 50the one or more results of the correlation performed by the correlationmodule 106 via a network interface 120.

In the same or different implementations, the presentation module 108may include a display module 254 for displaying the one or more resultsof the correlation operations performed by the correlation module 106.For example, in the case where the computing device 10 is a localdevice, the display module 254 may be configured to display to the user20 b the one or more results of the correlation performed by thecorrelation module 106 via a user interface 122.

In some implementations, the presentation module 108 may include asequential relationship presentation module 256 configured to present anindication of a sequential relationship between at least one subjectiveuser state of a user 20* and at least one objective occurrenceassociated with the user 20* In some implementations, the presentationmodule 108 may include a prediction presentation module 258 configuredto present a prediction of a future subjective user state of a user 20*resulting from a future objective occurrence associated with the user20*. In the same or different implementations, the predictionpresentation module 258 may also be designed to present a prediction ofa future subjective user state of a user 20* resulting from a pastobjective occurrence associated with the user 20*. In someimplementations, the presentation module 108 may include a pastpresentation module 260 that is designed to present a past subjectiveuser state of a user 20* in connection with a past objective occurrenceassociated with the user 20*.

In some implementations, the presentation module 108 may include arecommendation module 262 that is configured to present a recommendationfor a future action based, at least in part, on the results of acorrelation of subjective user state data 60 with objective occurrencedata 70* performed by the correlation module 106. In certainimplementations, the recommendation module 262 may further include ajustification module 264 for presenting a justification for therecommendation presented by the recommendation module 262. In someimplementations, the presentation module 108 may include a strength ofcorrelation presentation module 266 for presenting an indication of astrength of correlation between subjective user state data 60 andobjective occurrence data 70*.

As will be further described herein, in some embodiments, thepresentation module 108 may be prompted to present the one or moreresults of a correlation operation performed by the correlation module106 in response to a reporting of one or more events, objectiveoccurrences, and/or subjective user states.

As briefly described earlier, in various embodiments, the computingdevice 10 may include a network interface 120 that may facilitate incommunicating with a user 20 a and/or one or more third parties 50. Forexample, in embodiments whereby the computing device 10 is a server, thecomputing device 10 may include a network interface 120 that may beconfigured to receive from the user 20 a subjective user state data 60.In some embodiments, objective occurrence data 70 a, 70 b, or 70 c mayalso be received through the network interface 120. Examples of anetwork interface 120 includes, for example, a network interface card(NIC).

The computing device 10, in various embodiments, may also include amemory 140 for storing various data. For example, in some embodiments,memory 140 may be employed in order to store subjective user state data61 of a user 20* that may indicate one or more past subjective userstates of the user 20* and objective occurrence data 70* associated withthe user 20* that may indicate one or more past objective occurrences.In some embodiments, memory 140 may store historical data 72 such ashistorical medical data of a user 20*(e.g., genetic, metabolome,proteome information), population trends, historical sequential patternsderived from general population, and so forth.

In various embodiments, the computing device 10 may include a userinterface 122 to communicate directly with a user 20 b. For example, inembodiments in which the computing device 10 is a local device, the userinterface 122 may be configured to directly receive from the user 20 bsubjective user state data 60. The user interface 122 may include, forexample, one or more of a display monitor, a touch screen, a key board,a key pad, a mouse, an audio system, an imaging system including adigital or video camera, and/or other user interface devices.

FIG. 2 e illustrates particular implementations of the one or moreapplications 126 of FIG. 1 b. For these implementations, the one or moreapplications 126 may include, for example, communication applicationssuch as a text messaging application and/or an audio messagingapplication including a voice recognition system application. In someimplementations, the one or more applications 126 may include a web 2.0application 266 to facilitate communication via, for example, the WorldWide Web. The functional roles of the various components, modules, andsub-modules of the computing device 10 presented thus far will bedescribed in greater detail with respect to the processes and operationsto be described herein. Note that the subjective user state data 60 maybe in a variety of forms including, for example, text messages (e.g.,blog entries, microblog entries, instant messages, text email messages,and so forth), audio messages, and/or images (e.g., an image capturinguser's facial expression or gestures).

FIG. 3 illustrates an operational flow 300 representing exampleoperations related to acquisition and correlation of subjective userstate data 60 and objective occurrence data 70* in accordance withvarious embodiments. In some embodiments, the operational flow 300 maybe executed by, for example, the computing device 10 of FIG. 1 b.

In FIG. 3 and in the following figures that include various examples ofoperational flows, discussions and explanations may be provided withrespect to the above-described exemplary environment of FIGS. 1 a and 1b, and/or with respect to other examples (e.g., as provided in FIGS. 2a-2 e) and contexts. However, it should be understood that theoperational flows may be executed in a number of other environments andcontexts, and/or in modified versions of FIGS. 1 a, 1 b, and 2 a-2 e.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in other orders than those which areillustrated, or may be performed concurrently.

Further, in FIG. 3 and in following figures, various operations may bedepicted in a box-within-a-box manner. Such depictions may indicate thatan operation in an internal box may comprise an optional exampleembodiment of the operational step illustrated in one or more externalboxes. However, it should be understood that internal box operations maybe viewed as independent operations separate from any associatedexternal boxes and may be performed in any sequence with respect to allother illustrated operations, or may be performed concurrently.

In any event, after a start operation, the operational flow 300 may moveto a subjective user state data acquisition operation 302 for acquiringsubjective user state data including data indicating at least onesubjective user state associated with a user. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 of FIG. 1 b acquiring (e.g., receiving via network interface120 or via user interface 122) subjective user state data 60 includingdata indicating at least one subjective user state 60 a (e.g., asubjective mental state, a subjective physical state, or a subjectiveoverall state) associated with a user 20*.

Operational flow 300 may also include an objective occurrence dataacquisition operation 304 for acquiring objective occurrence dataincluding data indicating at least one objective occurrence associatedwith the user. For instance, the objective occurrence data acquisitionmodule 104 of the computing device 10 acquiring, via the networkinterface 120 or via the user interface 122, objective occurrence data70* including data indicating at least one objective occurrence (e.g.,ingestion of a food, medicine, or nutraceutical) associated with theuser 20*. Note that, and as those skilled in the art will recognize, thesubjective user state data acquisition operation 302 does not have to beperformed prior to the objective occurrence data acquisition operation304 and may be performed subsequent to the performance of the objectiveoccurrence data acquisition operation 304 or may be performedconcurrently with the objective occurrence data acquisition operation304.

Operational flow 300 may further include a correlation operation 306 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on a determination of at least onesequential pattern associated with the at least one subjective userstate and the at least one objective occurrence. For instance, thecorrelation module 106 of the computing device 10 correlating thesubjective user state data 60 with the objective occurrence data 70*based, at least in part, on a determination of at least one sequentialpattern (e.g., time sequential pattern) associated with the at least onesubjective user state (e.g., user feeling “tired”) and the at least oneobjective occurrence (e.g., high blood sugar level).

Finally, the operational flow 300 may include a presentation operation308 for presenting one or more results of the correlating. For instance,the presentation module 108 of the computing device 10 presenting, viathe network interface 120 or via the user interface 122, one or moreresults (e.g., in the form of a recommendation for a future action or inthe form of a notification of a past event) of the correlating performedby the correlation operation 306.

In various implementations, the subjective user state data acquisitionoperation 302 may include one or more additional operations asillustrated in FIGS. 4 a, 4 b, 4 c, 4 d, and 4 e. For example, in someimplementations the subjective user state data acquisition operation 302may include a reception operation 402 for receiving the subjective userstate data as depicted in FIGS. 4 a and 4 b. For instance, thesubjective user state data reception module 202 of the computing device10 receiving (e.g., via network interface 120 or via the user interface122) the subjective user state data 60.

The reception operation 402 may, in turn, further include one or moreadditional operations. For example, in some implementations, thereception operation 402 may include an operation 404 for receiving thesubjective user state data via a user interface as depicted in FIG. 4 a.For instance, the user interface data reception module 204 of thecomputing device 10 receiving the subjective user state data 60 via auser interface 122 (e.g., a keypad, a keyboard, a display monitor, atouchscreen, a mouse, an audio system including a microphone, an imagecapturing system including a video or digital camera, and/or otherinterface devices).

In some implementations, the reception operation 402 may include anoperation 406 for receiving the subjective user state data via a networkinterface as depicted in FIG. 4 a. For instance, the network interfacedata reception module 206 of the computing device 10 receiving thesubjective user state data 60 via a network interface 120 (e.g., a NIC).

In various implementations, operation 406 may further include one ormore operations. For example, in some implementations operation 406 mayinclude an operation 408 for receiving data indicating the at least onesubjective user state via an electronic message generated by the user asdepicted in FIG. 4 a. For instance, the network interface data receptionmodule 206 of the computing device 10 receiving data indicating the onesubjective user state 60 a (e.g., subjective mental state such asfeelings of happiness, sadness, anger, frustration, mental fatigue,drowsiness, alertness, and so forth) via an electronic message (e.g.,email, IM, or text message) generated by the user 20 a.

In some implementations, operation 406 may include an operation 410 forreceiving data indicating the at least one subjective user state via ablog entry generated by the user as depicted in FIG. 4 a. For instance,the network interface data reception module 206 of the computing device10 receiving data indicating the at least one subjective user state 60 a(e.g., subjective physical state such as physical exhaustion, physicalpain such as back pain or toothache, upset stomach, blurry vision, andso forth) via a blog entry such as a microblog entry generated by theuser 20 a.

In some implementations, operation 406 may include an operation 412 forreceiving data indicating the at least one subjective user state via astatus report generated by the user as depicted in FIG. 4 a. Forinstance, the network interface data reception module 206 of thecomputing device 10 receiving data indicating the at least onesubjective user state 60 a (e.g., subjective overall state of the user20* such as good, bad, well, exhausted, and so forth) via a statusreport (e.g., social network status report) generated by the user 20 a.

In some implementations, the reception operation 402 may include anoperation 414 for receiving subjective user state data including dataindicating at least one subjective user state specified by a selectionmade by the user, the selection being a selection of a subjective userstate from a plurality of alternative subjective user states as depictedin FIG. 4 a. For instance, the subjective user state data receptionmodule 202 of the computing device 10 receiving subjective user statedata 60 including data indicating at least one subjective user statespecified by a selection (e.g., via mobile device 30 or via userinterface 122) made by the user 20*, the selection being a selection ofa subjective user state from a plurality of alternative subjective userstates (e.g., as indicated by the mobile device 30 or by the userinterface 122).

Operation 414 may include one or more additional operations in variousalternative implementations. For example, in some implementations,operation 414 may include an operation 416 for receiving subjective userstate data including data indicating at least one subjective user statespecified by a selection made by the user, the selection being aselection of a subjective user state from two alternative contrastingsubjective user states as depicted in FIG. 4 a. For instance, thesubjective user state data reception module 202 of the computing device10 receiving subjective user state data 60 including data indicating atleast one subjective user state 60 a specified (e.g., via the mobiledevice 30 or via the user interface 122) by a selection made by the user20*, the selection being a selection of a subjective user state from twoalternative contrasting subjective user states (e.g., user in pain ornot in pain).

In some implementations, operation 414 may include an operation 417 forreceiving the selection via a network interface as depicted in FIG. 4 a.For instance, the network interface data reception module 206 of thecomputing device 10 receiving the selection of a subjective user state(e.g., a subjective mental state, a subjective physical state, or asubjective overall state) via a network interface 120.

In some implementations, operation 414 may include an operation 418 forreceiving the selection via user interface as depicted in FIG. 4 a. Forinstance, the user interface data reception module 204 of the computingdevice 10 receiving the selection of a subjective user state (e.g., asubjective mental state, a subjective physical state, or a subjectiveoverall state) via a user interface 122.

In some implementations, the reception operation 402 may include anoperation 420 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on a text entry provided by the user as depicted in FIG. 4 b. Forinstance, the text entry data reception module 208 of the computingdevice 10 receiving data indicating at least one subjective user state60 a (e.g., a subjective mental state, a subjective physical state, or asubjective overall state) associated with the user 20* that was obtainedbased, at least in part, on a text entry provided by the user 20*(e.g.,a text message provided by the user 20* via the mobile device 10 or viathe user interface 122).

In some implementations, the reception operation 402 may include anoperation 422 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on an audio entry provided by the user as depicted in FIG. 4 b.For instance, the audio entry data reception module 210 of the computingdevice 10 receiving data indicating at least one subjective user state60 a (e.g., a subjective mental state, a subjective physical state, or asubjective overall state) associated with the user 20* that was obtainedbased, at least in part, on an audio entry provided by the user20*(e.g., audio recording made via the mobile device 30 or via the userinterface 122).

In some implementations, the reception operation 402 may include anoperation 424 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on an image entry provided by the user as depicted in FIG. 4 b.For instance, the image entry data reception module 212 of the computingdevice 10 receiving data indicating at least one subjective user state60 a (e.g., a subjective mental state, a subjective physical state, or asubjective overall state) associated with the user 20* that was obtainedbased, at least in part, on an image entry provided by the user20*(e.g., one or more images recorded via the mobile device 30 or viathe user interface 122).

Operation 424 may further include one or more additional operations invarious alternative implementations. For example, in someimplementations, operation 424 may include an operation 426 forreceiving data indicating at least one subjective user state associatedwith the user that was obtained based, at least in part, on an imageentry showing a gesture made by the user as depicted in FIG. 4 b. Forinstance, the image entry data reception module 212 of the computingdevice 10 receiving data indicating at least one subjective user state60 a (e.g., a subjective user state such as “user is good” or “user isnot good”) associated with the user 20* that was obtained based, atleast in part, on an image entry showing a gesture (e.g., a thumb up ora thumb down) made by the user 20*.

In some implementations, operation 424 may include an operation 428 forreceiving data indicating at least one subjective user state associatedwith the user that was obtained based, at least in part, on an imageentry showing an expression made by the user as depicted in FIG. 4 b.For instance, the image entry data reception module 212 of the computingdevice 10 receiving data indicating at least one subjective user state60 a (e.g., a subjective mental state such as happiness or sadness)associated with the user 20* that was obtained based, at least in part,on an image entry showing an expression (e.g., a smile or a frownexpression) made by the user 20*.

In some implementations, the reception operation 402 may include anoperation 430 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on data provided through user interaction with a user interface asdepicted in FIG. 4 b. For instance, the subjective user state datareception module 202 of the computing device 10 receiving dataindicating at least one subjective user state 60 a associated with theuser 20* that was obtained based, at least in part, on data providedthrough user interaction (e.g., user 20* selecting one subjective userstate from a plurality of alternative subjective user states) with auser interface 122 of the computing device 10 or with a user interface122 of the mobile device 30.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 432 for acquiring data indicatingat least one subjective mental state of the user as depicted in FIG. 4b. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) data indicating at least one subjective mentalstate (e.g., sadness, happiness, alertness or lack of alertness, anger,frustration, envy, hatred, disgust, and so forth) of the user 20*.

In some implementations, operation 432 may further include an operation434 for acquiring data indicating at least a level of the one subjectivemental state of the user as depicted in FIG. 4 b. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring data indicating at least a level of the onesubjective mental state (e.g., extreme sadness or slight sadness) of theuser 20*.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 436 for acquiring data indicatingat least one subjective physical state of the user as depicted in FIG. 4b. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) data indicating at least one subjective physicalstate (e.g., blurry vision, physical pain such as backache or headache,upset stomach, physical exhaustion, and so forth) of the user 20*.

In some implementations, operation 436 may further include an operation438 for acquiring data indicating at least a level of the one subjectivephysical state of the user as depicted in FIG. 4 b. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring data indicating at least a level of the onesubjective physical state (e.g., a slight headache or a severe headache)of the user 20*.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 440 for acquiring data indicatingat least one subjective overall state of the user as depicted in FIG. 4c. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) data indicating at least one subjective overallstate (e.g., good, bad, wellness, hangover, fatigue, nausea, and soforth) of the user 20*. Note that a subjective overall state, as usedherein, may be in reference to any subjective user state that may notfit neatly into the categories of subjective mental state or subjectivephysical state.

In some implementations, operation 440 may further include an operation442 for acquiring data indicating at least a level of the one subjectiveoverall state of the user as depicted in FIG. 4 c. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring data indicating at least a level of the onesubjective overall state (e.g., a very bad hangover) of the user 20*.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 444 for acquiring subjective userstate data including data indicating at least a second subjective userstate associated with the user as depicted in FIG. 4 c. For instance,the subjective user state data acquisition module 102 of the computingdevice 10 acquiring subjective user state data 60 including dataindicating at least a second subjective user state 60 b (e.g., asubjective mental state, a subjective physical state, or a subjectiveoverall state) associated with the user 20*.

In various alternative implementations, operation 444 may include one ormore additional operations. For example, in some implementations,operation 444 includes an operation 446 for acquiring subjective userstate data including data indicating at least a second subjective userstate that is equivalent to the at least one subjective user state asdepicted in FIG. 4 c. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) subjective user statedata 60 including data indicating at least a second subjective userstate 60 b (e.g., anger) that is equivalent to the at least onesubjective user state (e.g., anger).

In some implementations, operation 446 may further include an operation448 for acquiring subjective user state data including data indicatingat least a second subjective user state that is at least proximatelyequivalent in meaning to the at least one subjective user state asdepicted in FIG. 4 c. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring subjectiveuser state data 60 including data indicating at least a secondsubjective user state 60 b (e.g., rage or fury) that is at leastproximately equivalent in meaning to the at least one subjective userstate (e.g., anger).

In some implementations, operation 444 includes an operation 450 foracquiring subjective user state data including data indicating at leasta second subjective user state that is proximately equivalent to the atleast one subjective user state as depicted in FIG. 4 c. For instance,the subjective user state data acquisition module 102 of the computingdevice 10 acquiring subjective user state data 60 including dataindicating at least a second subjective user state 60 b (e.g., feelingvery nauseous) that is proximately equivalent to the at least onesubjective user state (e.g., feeling extremely nauseous).

In some implementations, operation 444 includes an operation 451 foracquiring subjective user state data including data indicating at leasta second subjective user state that is a contrasting subjective userstate from the at least one subjective user state as depicted in FIG. 4c. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring subjective user state data 60including data indicating at least a second subjective user state 60 b(e.g., feeling very nauseous) that is a contrasting subjective userstate from the at least one subjective user state (e.g., feelingslightly nauseous or feeling not nauseous at all).

In some implementations, operation 444 includes an operation 452 foracquiring subjective user state data including data indicating at leasta second subjective user state that references the at least onesubjective user state as depicted in FIG. 4 c. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring subjective user state data 60 including dataindicating at least a second subjective user state 60 b that referencesthe at least one subjective user state (e.g., “I feel as good asyesterday” or “I am more tired than yesterday”).

In some implementations, operation 452 may further include an operation453 for acquiring subjective user state data including data indicatingat least a second subjective user state that is one of modification,extension, improvement, or regression of the at least one subjectiveuser state as depicted in FIG. 4 c. For instance, the subjective userstate data acquisition module 102 of the computing device 10 acquiringsubjective user state data 60 including data indicating at least asecond subjective user state 60 b that is one of a modification (e.g.,“my headache from yesterday has turned into a migraine”), extension(e.g., “I still have my backache from yesterday”), improvement (e.g., “Ifeel better than yesterday”), or regression (e.g., “I feel more tiredthan yesterday”) of the at least one subjective user state.

In some implementations the subjective user state data acquisitionoperation 302 of FIG. 3 may include an operation 454 for acquiring atime stamp associated with the at least one subjective user state asdepicted in FIG. 4 d. For instance, the time stamp acquisition module222 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122 as provided by the user 20*or by automatically generating) a time stamp (e.g., 10 PM Aug. 4, 2009)associated with the at least one subjective user state.

Operation 454 may further include, in various implementations, anoperation 455 for acquiring another time stamp associated with a secondsubjective user state indicated by the subjective user state data asdepicted in FIG. 4 d. For instance, the time stamp acquisition module222 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122 as provided by the user 20*or by automatically generating) another time stamp (e.g., 8 PM Aug. 12,2009) associated with a second subjective user state indicated by thesubjective user state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 456 for acquiring an indicationof a time interval associated with the at least one subjective userstate as depicted in FIG. 4 d. For instance, the time intervalacquisition module 223 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122 as provided bythe user 20* or by automatically generating) an indication of a timeinterval (e.g., 8 AM to 10 AM Jul. 24, 2009) associated with the atleast one subjective user state.

Operation 456 may further include, in various implementations, anoperation 457 for acquiring another indication of another time intervalassociated with a second subjective user state indicated by thesubjective user state data as depicted in FIG. 4 d. For instance, thetime interval acquisition module 223 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122 as provided by the user 20* or by automatically generating) anotherindication of another time interval (e.g., 2 PM to 8 PM Jul. 24, 2009)associated with a second subjective user state indicated by thesubjective user state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 458 for acquiring an indicationof a temporal relationship between the at least one subjective userstate and the at least one objective occurrence as depicted in FIG. 4 d.For instance, the temporal relationship acquisition module 224 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122 as provided by the user 20* or byautomatically generating) an indication of a temporal relationshipbetween the at least one subjective user state (e.g., easing of aheadache) and the at least one objective occurrence (e.g., ingestion ofaspirin). For example, acquiring an indication that a user's headacheeased after taking an aspirin.

Operation 458 may further include, in various implementations, anoperation 459 for acquiring an indication of a temporal relationshipbetween the at least one subjective user state and a second subjectiveuser state indicated by the subjective user state data as depicted inFIG. 4 d. For instance, the temporal relationship acquisition module 224of the computing device 10 acquiring (e.g., via the network interface120 or via the user interface 122 as provided by the user 20* or byautomatically generating) an indication of a temporal relationshipbetween the at least one subjective user state (e.g., tired) and asecond subjective user state (e.g., energetic) indicated by thesubjective user state data 60. For example, acquiring an indication thata user 20* felt tired before feeling energetic, or an indication thatthe user 20* felt energetic after feeling tired.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 460 for soliciting from the userthe at least one subjective user state as depicted in FIG. 4 d. Forinstance, the subjective user state data solicitation module 214 of thecomputing device 10 soliciting (e.g., via an inquiry to the user 20* toprovide a subjective user state) from the user 20* the at least onesubjective user state. In some implementations, the solicitation of theat least one subjective user state may involve requesting the user 20*to select at least one subjective user state from a plurality ofalternative subjective user states.

Operation 460 may further include, in some implementations, an operation462 for transmitting to the user a request for a subjective user stateas depicted in FIG. 4 d. For instance, the transmission module 216 ofthe computing device 10 transmitting (e.g., via the wireless and/orwired network 40) to the user 20* a request for a subjective user statesuch as the case when the computing device 10 is a server.Alternatively, such a request may be displayed via a user interface 122in cases where, for example, the computing device 10 is a local devicesuch as a handheld device.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 463 for acquiring the subjectiveuser state data at a server as depicted in FIG. 4 d. For instance, whenthe computing device 10 is a network server and is acquiring thesubjective user state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 464 for acquiring the subjectiveuser state data at a handheld device as depicted in FIG. 4 d. Forinstance, when the computing device 10 is a handheld device such as amobile phone or a PDA and is acquiring the subjective user state data60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 466 for acquiring the subjectiveuser state data at a peer-to-peer network component device as depictedin FIG. 4 d. For instance, when the computing device 10 is apeer-to-peer network component device and is acquiring the subjectiveuser state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 468 for acquiring the subjectiveuser state data via a Web 2.0 construct as depicted in FIG. 4 d. Forinstance, when the computing device 10 employs a Web 2.0 application inorder to acquire the subjective user state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 470 for acquiring data indicatingone subjective user state that occurred at least partially concurrentlywith an incidence of one objective occurrence associated with the useras depicted in FIG. 4 e. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring (e.g., via anetwork interface 120 or a user interface 122) data indicating onesubjective user state (e.g., feeling aggravated) that occurred at leastpartially concurrently with an incidence of one objective occurrence(e.g., in-laws visiting) associated with the user 20*.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 472 for acquiring data indicatingone subjective user state that occurred prior to an incidence of oneobjective occurrence associated with the user as depicted in FIG. 4 e.For instance, the subjective user state data acquisition module 102 ofthe computing device 10 acquiring (e.g., via a network interface 120 ora user interface 122) data indicating one subjective user state (e.g.,fear) that occurred prior to an incidence of one objective occurrence(e.g., meeting with the boss) associated with the user 20*.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 474 for acquiring data indicatingone subjective user state that occurred subsequent to an incidence ofone objective occurrence associated with the user as depicted in FIG. 4e. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via a network interface 120or a user interface 122) data indicating one subjective user state(e.g., easing of a headache) that occurred subsequent to an incidence ofone objective occurrence (e.g., consuming a particular brand of aspirin)associated with the user 20*.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 476 for acquiring data thatindicates one subjective user state that occurred within a predefinedtime period of an incidence of one objective occurrence associated withthe user as depicted in FIG. 4 e. For instance, the subjective userstate data acquisition module 102 of the computing device 10 acquiring(e.g., via a network interface 120 or a user interface 122) dataindicating one subjective user state (e.g., easing of a backache) thatoccurred within a predefined time period (e.g., three hours) of anincidence of one objective occurrence (e.g., ingestion of a dose ofibuprofen) associated with the user 20*.

Referring back to FIG. 3, the objective occurrence data acquisitionoperation 304 in various embodiments may include one or more additionaloperations as illustrated in FIGS. 5 a to 5 k. For example, in someimplementations, the objective occurrence data acquisition operation 304may include a reception operation 500 for receiving the objectiveoccurrence data as depicted in FIG. 5 a. For instance, the objectiveoccurrence data reception module 226 (see FIG. 2 b) of the computingdevice 10 receiving (e.g., via the network interface 120 or via the userinterface 122) the objective occurrence data 70*.

The reception operation 500 in various implementations may include oneor more additional operations. For example, in some implementations thereception operation 500 may include an operation 501 for receiving theobjective occurrence data from at least one of a wireless network or awired network as depicted in FIG. 5 a. For instance, the objectiveoccurrence data reception module 226 of the computing device 10receiving (e.g., via the network interface 120) the objective occurrencedata 70* from at least one of a wireless network or a wired network.

In some implementations, the reception operation 500 may include anoperation 502 for receiving the objective occurrence data via one ormore blog entries as depicted in FIG. 5 a. For instance, the objectiveoccurrence data reception module 226 of the computing device 10receiving (e.g., via the network interface 120) the objective occurrencedata 70* via one or more blog entries (e.g., microblog entries).

In some implementations, the reception operation 500 may include anoperation 503 for receiving the objective occurrence data via one ormore status reports as depicted in FIG. 5 a. For instance, the objectiveoccurrence data reception module 226 of the computing device 10receiving (e.g., via the network interface 120) the objective occurrencedata 70* via one or more status reports (e.g., social networking statusreports).

In some implementations, the reception operation 500 may include anoperation 504 for receiving the objective occurrence data via a Web 2.0construct as depicted in FIG. 5 a. For instance, the objectiveoccurrence data reception module 226 of the computing device 10receiving (e.g., via the network interface 120) the objective occurrencedata 70* via a Web 2.0 construct (e.g., Web 2.0 application).

In some implementations, the reception operation 500 may include anoperation 505 for receiving the objective occurrence data from one ormore third party sources as depicted in FIG. 5 a. For instance, theobjective occurrence data reception module 226 of the computing device10 receiving (e.g., via the network interface 120) the objectiveoccurrence data 70* from one or more third party sources (e.g., a healthcare professional, a pharmacy, a hospital, a health care organization, ahealth monitoring service, a health care clinic, a school, a place ofemployment, a social group, a content provider, and so forth).

In some implementations, the reception operation 500 may include anoperation 506 for receiving the objective occurrence data from one ormore sensors configured to sense one or more objective occurrencesassociated with the user as depicted in FIG. 5 a. For instance, theobjective occurrence data reception module 226 of the computing device10 receiving (e.g., via the network interface 120) the objectiveoccurrence data 70* from one or more sensors 35 (e.g., a physiologicalsensing device, a physical activity sensing device such as a pedometer,a GPS, and so forth) configured to sense one or more objectiveoccurrences associated with the user 20*.

In some implementations, the reception operation 500 may include anoperation 507 for receiving the objective occurrence data from the useras depicted in FIG. 5 a. For instance, the objective occurrence datareception module 226 of the computing device 10 receiving (e.g., via thenetwork interface 120 or the user interface 122) the objectiveoccurrence data 70* from the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 508 for acquiring objectiveoccurrence data including data indicating at least a second objectiveoccurrence associated with the user as depicted in FIG. 5 b. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122) objective occurrence data 70* including dataindicating at least a second objective occurrence associated with theuser 20*.

In various implementations, operation 508 may further include one ormore additional operations. For example, in some implementations,operation 508 may include an operation 509 for acquiring objectiveoccurrence data including data indicating one objective occurrenceassociated with a first point in time and data indicating a secondobjective occurrence associated with a second point in time as depictedin FIG. 5 b. For instance, the objective occurrence data acquisitionmodule 104 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122) objective occurrence data70* including data indicating one objective occurrence (e.g., firstmeeting with the boss) associated with a first point in time (e.g., 8 AMTuesday Oct. 10, 2009) and data indicating a second objective occurrence(e.g., second meeting with the boss) associated with a second point intime (e.g., 3 PM Friday Oct. 13, 2009).

In some implementations, operation 508 may include an operation 510 foracquiring objective occurrence data including data indicating oneobjective occurrence associated with a first time interval and dataindicating a second objective occurrence associated with a second timeinterval as depicted in FIG. 5 b. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) objectiveoccurrence data 70* including data indicating one objective occurrence(e.g., jogging) associated with a first time interval (e.g., 7 PM to 8PM Aug. 4, 2009) and data indicating a second objective occurrence(e.g., jogging) associated with a second time interval (e.g., 6 PM to6:30 PM Aug. 12, 2009).

In some implementations, operation 508 may include an operation 511 foracquiring objective occurrence data including data indicating at least asecond objective occurrence that is equivalent to the at least oneobjective occurrence as depicted in FIG. 5 b. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) objective occurrence data 70* including data indicatingat least a second objective occurrence (e.g., consuming three tablets ofibuprofen) that is equivalent to the at least one objective occurrence(e.g., consuming three tablets of ibuprofen).

Operation 511 in certain implementations may further include anoperation 512 for acquiring objective occurrence data including dataindicating at least a second objective occurrence that is at leastproximately equivalent in meaning to the at least one objectiveoccurrence as depicted in FIG. 5 b. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) objective occurrence data 70* including data indicating at least asecond objective occurrence (e.g., cloudy day) that is at leastproximately equivalent in meaning to the at least one objectiveoccurrence (e.g., overcast day).

In some implementations, operation 508 may include an operation 513 foracquiring objective occurrence data including data indicating at least asecond objective occurrence that is proximately equivalent to the atleast one objective occurrence as depicted in FIG. 5 b. For instance,the objective occurrence data acquisition module 104 of the computingdevice 10 acquiring (e.g., via the network interface 120 or via the userinterface 122) objective occurrence data 70* including data indicatingat least a second objective occurrence (e.g., consuming three tablets ofbrand x ibuprofen) that is proximately equivalent to the one at leastobjective occurrence (e.g., consuming three tablets of brand yibuprofen).

In some implementations, operation 508 may include an operation 514 foracquiring objective occurrence data including data indicating at least asecond objective occurrence that is a contrasting objective occurrencefrom the at least one objective occurrence as depicted in FIG. 5 c. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122) objective occurrence data 70* including dataindicating at least a second objective occurrence (e.g., consuming threetablets of brand x ibuprofen) that is a contrasting objective occurrencefrom the at least one objective occurrence (e.g., consuming one tabletof brand x ibuprofen or consuming no brand x ibuprofen tablets).

In some implementations, operation 508 may include an operation 515 foracquiring objective occurrence data including data indicating at least asecond objective occurrence that references the at least one objectiveoccurrence as depicted in FIG. 5 c. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) objective occurrence data 70* including data indicating at least asecond objective occurrence (e.g., today's temperature is the same asyesterday's) that references the at least one objective occurrence(e.g., 94 degrees).

Operation 515 may include one or more additional operations in variousalternative implementations. For example, in some implementations,operation 515 may include an operation 516 for acquiring objectiveoccurrence data including data indicating at least a second objectiveoccurrence that is a comparison to the at least one objective occurrenceas depicted in FIG. 5 c. For instance, the objective occurrence dataacquisition module 104 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122) objectiveoccurrence data 70* including data indicating at least a secondobjective occurrence (e.g., today's temperature is 10 degrees hotterthan yesterday's) that is a comparison to the at least one objectiveoccurrence (e.g., 84 degrees).

In some implementations, operation 515 may include an operation 517 foracquiring objective occurrence data including data indicating at least asecond objective occurrence that is a modification of the at least oneobjective occurrence as depicted in FIG. 5 c. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) objective occurrence data 70* including data indicatingat least a second objective occurrence (e.g., the rain showers yesterdayhas changed over to a snow storm) that is a modification of the at leastone objective occurrence (e.g., rain showers).

In some implementations, operation 515 may include an operation 518 foracquiring objective occurrence data including data indicating at least asecond objective occurrence that is an extension of the at least oneobjective occurrence as depicted in FIG. 5 c. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) objective occurrence data 70* including data indicatingat least a second objective occurrence (e.g., my high blood pressurefrom yesterday is still present) that is an extension of the at leastone objective occurrence (e.g., high blood pressure).

In various implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 519 for acquiring atime stamp associated with the at least one objective occurrence asdepicted in FIG. 5 d. For instance, the time stamp acquisition module230 (see FIG. 2 b) of the computing device 10 acquiring (e.g., via thenetwork interface 120 or via the user interface 122 as provided by theuser 20* or by automatically generating) a time stamp associated withthe at least one objective occurrence.

Operation 519 in some implementations may further include an operation520 for acquiring another time stamp associated with a second objectiveoccurrence indicated by the objective occurrence data as depicted inFIG. 5 d. For instance, the time stamp acquisition module 230 (see FIG.2 b) of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122 as provided by the user 20*or by automatically generating) another time stamp associated with asecond objective occurrence indicated by the objective occurrence data70*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 521 for acquiring an indicationof a time interval associated with the at least one objective occurrenceas depicted in FIG. 5 d. For instance, the time interval acquisitionmodule 231 (see FIG. 2 b) of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122 as providedby the user 20* or by automatically generating) an indication of a timeinterval associated with the at least one objective occurrence.

Operation 521 in some implementations may further include an operation522 for acquiring another indication of another time interval associatedwith a second objective occurrence indicated by the objective occurrencedata as depicted in FIG. 5 d. For instance, the time intervalacquisition module 231 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122 as provided bythe user 20* or by automatically generating) another indication ofanother time interval associated with a second objective occurrenceindicated by the objective occurrence data 70*.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 523 for acquiring anindication of at least a temporal relationship between the at least oneobjective occurrence and a second objective occurrence indicated by theobjective occurrence data as depicted in FIG. 5 d. For instance, thetemporal relationship acquisition module 232 (see FIG. 2 b) of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122 as provided by the user 20* or byautomatically generating) an indication of at least a temporalrelationship between the at least one objective occurrence (e.g.,drinking a soda right after eating a chocolate sundae) and a secondobjective occurrence (e.g., eating the chocolate sundae) indicated bythe objective occurrence data 70*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 524 for acquiring data indicatingat least one objective occurrence associated with the user and one ormore attributes associated with the at least one objective occurrence asdepicted in FIG. 5 d. For instance, the objective occurrence dataacquisition module 104 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122) data indicatingat least one objective occurrence (e.g., exercising on an exercisingmachine) associated with the user 20* and one or more attributes (e.g.,type of exercising machine or length of time on the exercise machine)associated with the at least one objective occurrence.

In various implementations, the objective occurrence data acquisitionoperation 304 may include an operation 525 for acquiring data indicatingat least one objective occurrence of an ingestion by the user of amedicine as depicted in FIG. 5 e. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an ingestion by the user20* of a medicine (e.g., a dosage of a beta blocker).

Operation 525 may further include, in some implementations, an operation526 for acquiring data indicating another objective occurrence ofanother ingestion by the user of another medicine as depicted in FIG. 5e. For instance, the objective occurrence data acquisition module 104 ofthe computing device 10 acquiring (e.g., via the network interface 120or via the user interface 122) data indicating another objectiveoccurrence of another ingestion by the user 20* of another medicine(e.g., another ingestion of the beta blocker, an ingestion of anothertype of beta blocker, or ingestion of a completely different type ofmedicine).

Operation 526 may further include, in some implementations, an operation527 for acquiring data indicating at least one objective occurrence ofan ingestion by the user of a medicine and data indicating anotherobjective occurrence of another ingestion by the user of anothermedicine, the ingestions of the medicine and the another medicine beingingestions of same or similar type of medicine as depicted in FIG. 5 e.For instance, the objective occurrence data acquisition module 104 ofthe computing device 10 acquiring (e.g., via the network interface 120or via the user interface 122) data indicating at least one objectiveoccurrence of an ingestion by the user 20* of a medicine (e.g., aningestion of a generic brand of beta blocker) and data indicatinganother objective occurrence of another ingestion by the user 20* ofanother medicine (e.g., another ingestion of the same generic brand ofbeta blocker or a different brand of the same type of beta blocker), theingestions of the medicine and the another medicine being ingestions ofsame or similar type of medicine.

In some implementations, operation 527 may further include an operation528 for acquiring data indicating at least one objective occurrence ofan ingestion by the user of a medicine and data indicating anotherobjective occurrence of another ingestion by the user of anothermedicine, the ingestions of the medicine and the another medicine beingingestions of same or similar quantities of the same or similar type ofmedicine as depicted in FIG. 5 e. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an ingestion by the user20* of a medicine (e.g., 5 units of a generic brand of beta blocker) anddata indicating another objective occurrence of another ingestion by theuser 20* of another medicine (e.g., another 5 units of the same genericbrand of beta blocker), the ingestions of the medicine and the anothermedicine being ingestions of same or similar quantities of the same orsimilar type of medicine.

In some alternative implementations, operation 526 may include anoperation 529 for acquiring data indicating at least one objectiveoccurrence of an ingestion by the user of a medicine and data indicatinganother objective occurrence of another ingestion by the user of anothermedicine, the ingestions of the medicine and the another medicine beingingestions of different types of medicine as depicted in FIG. 5 e. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122) data indicating at least one objectiveoccurrence of an ingestion by the user 20* of a medicine (e.g., aningestion of a particular type of beta blocker) and data indicatinganother objective occurrence of another ingestion by the user of anothermedicine (e.g., an ingestion of another type of beta blocker or aningestion of a completely different type of medicine), the ingestions ofthe medicine and the another medicine being ingestions of differenttypes of medicine.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 530 for acquiring dataindicating at least one objective occurrence of an ingestion by the userof a food item as depicted in FIG. 5 f. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an ingestionby the user 20* of a food item (e.g., an apple).

Operation 530 may, in turn, include an operation 531 for acquiring dataindicating another objective occurrence of another ingestion by the userof another food item as depicted in FIG. 5 f. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) another objective occurrence of another ingestion by theuser 20* of another food item (e.g., another apple, an orange, ahamburger, and so forth).

In some implementations, operation 531 may further include an operation532 for acquiring data indicating at least one objective occurrence ofan ingestion by the user of a food item and data indicating anotherobjective occurrence of another ingestion by the user of another fooditem, the ingestions of the food item and the another food item beingingestions of same or similar type of food item as depicted in FIG. 5 f.For instance, the objective occurrence data acquisition module 104 ofthe computing device 10 acquiring (e.g., via the network interface 120or via the user interface 122) data indicating at least one objectiveoccurrence of an ingestion by the user 20* of a food item (e.g., aMacintosh apple) and data indicating another objective occurrence ofanother ingestion by the user 20* of another food item (e.g., anotherMacintosh apple or a Fuji apple), the ingestions of the food item andthe another food item being ingestions of same or similar type of fooditem.

In some implementations, operation 532 may further include an operation533 for acquiring data indicating at least one objective occurrence ofan ingestion by the user of a food item and data indicating anotherobjective occurrence of another ingestion by the user of another fooditem, the ingestions of the food item and the another food item beingingestions of same or similar quantities of the same or similar type offood item as depicted in FIG. 5 f. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an ingestionby the user 20* of a food item (e.g., 10 ounces of a Macintosh apple)and data indicating another objective occurrence of another ingestion bythe user 20* of another food item (e.g., 10 ounces of another Macintoshapple or a Fuji apple), the ingestions of the food item and the anotherfood item being ingestions of same or similar quantities of the same orsimilar type of food item.

In some alternative implementations, operation 531 may include anoperation 534 for acquiring data indicating at least one objectiveoccurrence of an ingestion by the user of a food item and dataindicating another objective occurrence of another ingestion by the userof another food item, the ingestions of the food item and the anotherfood item being ingestions of different types of food item as depictedin FIG. 5 f. For instance, the objective occurrence data acquisitionmodule 104 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122) data indicating at leastone objective occurrence of an ingestion by the user 20* of a food item(e.g., an apple) and data indicating another objective occurrence ofanother ingestion by the user 20* of another food item (e.g., a banana),the ingestions of the food item and the another food item beingingestions of different types of food item.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 535 for acquiring dataindicating at least one objective occurrence of an ingestion by the userof a nutraceutical as depicted in FIG. 5 g. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an ingestionby the user 20* of a nutraceutical (e.g. broccoli).

Operation 535 in certain implementations may further include anoperation 536 for acquiring data indicating another objective occurrenceof another ingestion by the user of another nutraceutical as depicted inFIG. 5 g. For instance, the objective occurrence data acquisition module104 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122) data indicating anotherobjective occurrence of another ingestion by the user 20* of anothernutraceutical (e.g., another broccoli, red grapes, soy beans, or someother type of nutraceutical).

In some implementations, operation 536 may include an operation 537 foracquiring data indicating at least one objective occurrence of aningestion by the user of a nutraceutical and data indicating anotherobjective occurrence of another ingestion by the user of anothernutraceutical, the ingestions of the nutraceutical and the anothernutraceutical being ingestions of same or similar type of nutraceuticalas depicted in FIG. 5 g. For instance, the objective occurrence dataacquisition module 104 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122) data indicatingat least one objective occurrence of an ingestion by the user 20* of anutraceutical (e.g., red grapes) and data indicating another objectiveoccurrence of another ingestion by the user of another nutraceutical(e.g., red grapes), the ingestions of the nutraceutical and the anothernutraceutical being ingestions of same or similar type of nutraceutical.

Operation 537 may, in some instances, further include an operation 538for acquiring data indicating at least one objective occurrence of aningestion by the user of a nutraceutical and data indicating anotherobjective occurrence of another ingestion by the user of anothernutraceutical, the ingestions of the nutraceutical and the anothernutraceutical being ingestions of same or similar quantities of the sameor similar type of nutraceutical as depicted in FIG. 5 g. For instance,the objective occurrence data acquisition module 104 of the computingdevice 10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of aningestion by the user 20* of a nutraceutical (e.g., 12 ounces of redgrapes) and data indicating another objective occurrence of anotheringestion by the user 20* of another nutraceutical (e.g., 12 ounces ofred grapes), the ingestions of the nutraceutical and the anothernutraceutical being ingestions of same or similar quantities of the sameor similar type of nutraceutical.

In some alternative implementations, operation 536 may include anoperation 539 for acquiring data indicating at least one objectiveoccurrence of an ingestion by the user of a nutraceutical and dataindicating another objective occurrence of another ingestion by the userof another nutraceutical, the ingestions of the nutraceutical and theanother nutraceutical being ingestions of different types ofnutraceutical as depicted in FIG. 5 g. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an ingestionby the user 20* of a nutraceutical (e.g., red grapes) and dataindicating another objective occurrence of another ingestion by the user20* of another nutraceutical (e.g., soy beans), the ingestions of thenutraceutical and the another nutraceutical being ingestions ofdifferent types of nutraceutical.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 540 for acquiring dataindicating at least one objective occurrence of an exercise routineexecuted by the user as depicted in FIG. 5 h. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of anexercise routine (e.g., jogging) executed by the user 20*.

In various implementations, operation 540 may further include anoperation 541 for acquiring data indicating another objective occurrenceof another exercise routine executed by the user as depicted in FIG. 5h. For instance, the objective occurrence data acquisition module 104 ofthe computing device 10 acquiring (e.g., via the network interface 120or via the user interface 122) data indicating another objectiveoccurrence of another exercise routine (e.g., jogging again,weightlifting, aerobics, treadmill, or some other exercise routine)executed by the user 20*.

In some implementations, operation 541 may further include an operation542 for acquiring data indicating at least one objective occurrence ofan exercise routine executed by the user and data indicating anotherobjective occurrence of another exercise routine executed by the user,the exercise routines executed by the user being the same or similartype of exercise routine as depicted in FIG. 5 h. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of anexercise routine (e.g., working out on an elliptical machine) executedby the user 20* and data indicating another objective occurrence ofanother exercise routine (e.g., working out on a treadmill) executed bythe user 20*, the exercise routines executed by the user 20* being thesame or similar type of exercise routine.

In some implementations, operation 542 may further include an operation543 for acquiring data indicating at least one objective occurrence ofan exercise routine executed by the user and data indicating anotherobjective occurrence of another exercise routine executed by the user,the exercise routines executed by the user being the same or similarquantity of the same or similar type of exercise routine as depicted inFIG. 5 h. For instance, the objective occurrence data acquisition module104 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122) data indicating at leastone objective occurrence of an exercise routine (e.g., working out on anelliptical machine for 30 minutes) executed by the user 20* and dataindicating another objective occurrence of another exercise routine(e.g., working out on a treadmill for 27 minutes) executed by the user20*, the exercise routines executed by the user 20* being the same orsimilar quantity of the same or similar type of exercise routine.

In some implementations, operation 541 may include an operation 544 foracquiring data indicating at least one objective occurrence of anexercise routine executed by the user and data indicating anotherobjective occurrence of another exercise routine executed by the user,the exercise routines executed by the user being different types ofexercise routine as depicted in FIG. 5 h. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an exerciseroutine (e.g., working out on a treadmill) executed by the user 20* anddata indicating another objective occurrence of another exercise routine(e.g., lifting weights) executed by the user 20*, the exercise routinesexecuted by the user 20* being different types of exercise routine.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 545 for acquiring dataindicating at least one objective occurrence of a social activityexecuted by the user as depicted in FIG. 5 i. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of asocial activity (e.g., hiking with friends) executed by the user 20*.

In some implementations, operation 545 may further include an operation546 acquiring data indicating another objective occurrence of anothersocial activity executed by the user as depicted in FIG. 5 i. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122) data indicating another objective occurrenceof another social activity (e.g., hiking again with friends, skiing withfriends, dining with friends, and so forth) executed by the user 20*.

In some implementations, operation 546 may include an operation 547 foracquiring data indicating at least one objective occurrence of a socialactivity executed by the user and data indicating another objectiveoccurrence of another social activity executed by the user, the socialactivities executed by the user being same or similar type of socialactivities as depicted in FIG. 5 i. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of a socialactivity (e.g., dinner with friends) executed by the user 20* and dataindicating another objective occurrence of another social activity(e.g., another dinner with friends) executed by the user 20*, the socialactivities executed by the user 20* being same or similar type of socialactivities.

In some implementations, operation 546 may include an operation 548 foracquiring data indicating at least one objective occurrence of a socialactivity executed by the user and data indicating another objectiveoccurrence of another social activity executed by the user, the socialactivities executed by the user being different types of social activityas depicted in FIG. 5 i. For instance, the objective occurrence dataacquisition module 104 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122) data indicatingat least one objective occurrence of a social activity (e.g., dinnerwith friends) executed by the user 20* and data indicating anotherobjective occurrence of another social activity (e.g., dinner within-laws) executed by the user 20*, the social activities executed by theuser 20* being different types of social activity.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 549 for acquiring dataindicating at least one objective occurrence of an activity performed bya third party as depicted in FIG. 5 i. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an activity(e.g., boss on a vacation) performed by a third party 50.

Operation 549, in some instances, may further include an operation 550for acquiring data indicating another objective occurrence of anotheractivity performed by the third party as depicted in FIG. 5 i. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122) data indicating another objective occurrenceof another activity (e.g., boss on a vacation again, boss away fromoffice on business trip, or boss in the office) performed by the thirdparty 50.

In some implementations, operation 550 may include an operation 551 foracquiring data indicating at least one objective occurrence of anactivity performed by a third party and data indicating anotherobjective occurrence of another activity performed by the third party,the activities performed by the third party being same or similar typeof activities as depicted in FIG. 5 i. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an activity(e.g., boss away from office on business trip) performed by a thirdparty 50 and data indicating another objective occurrence of anotheractivity (e.g., boss again away from office on another business trip)performed by the third party 50, the activities performed by the thirdparty 50 being same or similar type of activities.

In some implementations, operation 550 may include an operation 552 foracquiring data indicating at least one objective occurrence of anactivity performed by a third party and data indicating anotherobjective occurrence of another activity performed by the third party,the activities performed by the third party being different types ofactivity as depicted in FIG. 5 i. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an activity (e.g., bossaway on vacation) performed by a third party 50 and data indicatinganother objective occurrence of another activity (e.g., boss returningto office from vacation) performed by the third party 50, the activitiesperformed by the third party 50 being different types of activity.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 553 for acquiring dataindicating at least one objective occurrence of a physicalcharacteristic of the user as depicted in FIG. 5 j. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of aphysical characteristic (e.g., a blood sugar level) of the user 20*.Note that a physical characteristic such as a blood sugar level could bedetermined using a device such as a blood sugar meter and then reportedby the user 20* or by a third party 50. Alternatively, such results maybe reported or provided directly by the meter.

Operation 553, in some instances, may further include an operation 554for acquiring data indicating another objective occurrence of anotherphysical characteristic of the user as depicted in FIG. 5 j. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring (e.g., via the network interface 120 orvia the user interface 122) data indicating another objective occurrenceof another physical characteristic (e.g., another blood sugar level or ablood pressure measurement) of the user 20*.

In some implementations, operation 554 may include an operation 555 foracquiring data indicating at least one objective occurrence of aphysical characteristic of the user and data indicating anotherobjective occurrence of another physical characteristic of the user, thephysical characteristics of the user being same or similar type ofphysical characteristic as depicted in FIG. 5 j. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of aphysical characteristic (e.g., blood sugar level of 220) of the user 20*and data indicating another objective occurrence of another physicalcharacteristic (e.g., blood sugar level of 218) of the user 20*, thephysical characteristics of the user 20* being same or similar type ofphysical characteristic.

In some implementations, operation 554 may include an operation 556 foracquiring data indicating at least one objective occurrence of aphysical characteristic of the user and data indicating anotherobjective occurrence of another physical characteristic of the user, thephysical characteristics of the user being different types of physicalcharacteristic as depicted in FIG. 5 j. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of a physicalcharacteristic (e.g., high blood pressure) of the user 20* and dataindicating another objective occurrence of another physicalcharacteristic (e.g., low blood pressure) of the user 20*, the physicalcharacteristics of the user 20* being different types of physicalcharacteristic.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 557 for acquiring data indicatingat least one objective occurrence of a resting, a learning, or arecreational activity by the user as depicted in FIG. 5 j. For instance,the objective occurrence data acquisition module 104 of the computingdevice 10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of aresting (e.g., sleeping), a learning (e.g., reading), or a recreationalactivity (e.g., a round of golf) by the user 20*.

Operation 557, in some instances, may further include an operation 558for acquiring data indicating another objective occurrence of anotherresting, another learning, or another recreational activity by the useras depicted in FIG. 5 j. For instance, the objective occurrence dataacquisition module 104 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122) data indicatinganother objective occurrence of another resting (e.g., watchingtelevision), another learning (e.g., attending a class or seminar), oranother recreational activity (e.g., another round of golf) by the user20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 559 for acquiring data indicatingat least one objective occurrence of an external event as depicted inFIG. 5 j. For instance, the objective occurrence data acquisition module104 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122) data indicating at leastone objective occurrence of an external event (e.g., rain storm).

Operation 559, in some instances, may further include an operation 560for acquiring data indicating another objective occurrence of anotherexternal event as depicted in FIG. 5 j. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating another objective occurrence of another externalevent (e.g., another rain storm or sunny clear weather).

In some implementations, operation 560 may include an operation 561 foracquiring data indicating at least one objective occurrence of anexternal event and data indicating another objective occurrence ofanother external event, the external events being same or similar typeof external event as depicted in FIG. 5 j. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an externalevent (e.g., rain storm) and data indicating another objectiveoccurrence of another external event (e.g., another rain storm), theexternal events being same or similar type of external event.

In some implementations, operation 560 may include an operation 562 foracquiring data indicating at least one objective occurrence of anexternal event and data indicating another objective occurrence ofanother external event, the external events being different types ofexternal event as depicted in FIG. 5 j. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an externalevent (e.g., rain storm) and data indicating another objectiveoccurrence of another external event (e.g., sunny clear weather), theexternal events being different types of external event.

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 563 for acquiring dataindicating at least one objective occurrence related to a location ofthe user as depicted in FIG. 5 k. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence related to a location(e.g., work office at a first point or interval in time) of the user20*. In some instances, such data may be provided by the user 20* viathe user interface 122 (e.g., in the case where the computing device 10is a local device) or via the mobile device 30 (e.g., in the case wherethe computing device 10 is a network server). Alternatively, such datamay be provided directly by a sensor device 35 such as a GPS device, orby a third party 50.

Operation 563, in some instances, may further include an operation 564for acquiring data indicating another objective occurrence related toanother location of the user as depicted in FIG. 5 k. For instance, theobjective occurrence data acquisition module 104 of the computing device10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating another objective occurrence related toanother location (e.g., work office or home at a second point orinterval in time) of the user 20*.

In some implementations, operation 564 may include an operation 565 foracquiring data indicating at least one objective occurrence related to alocation of the user and data indicating another objective occurrencerelated to another location of the user, the locations being same orsimilar location as depicted in FIG. 5 k. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence related to alocation (e.g., work office at a first point or interval in time) of theuser 20* and data indicating another objective occurrence related toanother location (e.g., work office at a second point or interval intime) of the user 20*, the locations being same or similar location.

In some implementations, operation 564 may include an operation 566 foracquiring data indicating at least one objective occurrence related to alocation of the user and data indicating another objective occurrencerelated to another location of the user, the locations being differentlocations as depicted in FIG. 5 k. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence related to alocation (e.g., work office at a first point or interval in time) of theuser 20* and data indicating another objective occurrence related toanother location (e.g., home at a second point or interval in time) ofthe user 20*, the locations being different locations.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 569 for soliciting the objectiveoccurrence data including data indicating at least one objectiveoccurrence associated with the user as depicted in FIG. 5 k. Forinstance, the objective occurrence data solicitation module 234 (seeFIG. 2 b) of the computing device 10 soliciting (e.g., via the userinterface 122 or transmitting a request via the network interface 120)the objective occurrence data 70* including data indicating at least oneobjective occurrence associated with the user 20*.

In various implementations, operation 569 may include one or moreadditional operations. For instance, in some implementations, operation569 may include an operation 570 for soliciting from the user theobjective occurrence data as depicted in FIG. 5 k. For instance, theobjective occurrence data solicitation module 234 of the computingdevice 10 soliciting (e.g., via the user interface 122 or bytransmitting a request via the network interface 120) from the user 20*the objective occurrence data 70*.

In some implementations, operation 569 may include an operation 571 forsoliciting from a third party source the objective occurrence data asdepicted in FIG. 5 k. For instance, the objective occurrence datasolicitation module 234 of the computing device 10 soliciting (e.g., bytransmitting a request via the network interface 120) from a third partysource (e.g., content provider, medical or dental entity, other users20* such as a spouse, a friend, or a boss, or other third party sources)the objective occurrence data 70 a.

In some implementations, operation 569 may include an operation 572 forsoliciting the objective occurrence data in response to a reporting of asubjective user state as depicted in FIG. 5 k. For instance, theobjective occurrence data solicitation module 234 of the computingdevice 10 soliciting (e.g., via the user interface 122 or bytransmitting a request via the network interface 120) the objectiveoccurrence data 70* in response to a reporting of a subjective userstate. For example, upon receiving a reporting of a hangover, asking theuser 20* whether the user 20* had drunk alcohol?

Referring back to FIG. 3, the correlation operation 306 may include oneor more additional operations in various alternative implementations.For example, in various implementations, the correlation operation 306may include an operation 604 for correlating the subjective user statedata with the objective occurrence data based, at least in part, on adetermination of whether the at least one subjective user state occurredwithin a predefined time increment from incidence of the at least oneobjective occurrence as depicted in FIG. 6 a. For instance, thecorrelation module 106 of the computing device 10 correlating thesubjective user state data 60 with the objective occurrence data 70*based, at least in part, on a determination by the “within predefinedtime increment determination” module 238 (see FIG. 2 c) of whether theat least one subjective user state occurred within a predefined timeincrement from incidence of the at least one objective occurrence.

In some implementations, the correlation operation 306 may include anoperation 608 for correlating the subjective user state data with theobjective occurrence data based, at least in part, on a determination ofwhether the at least one subjective user state occurred before, after,or at least partially concurrently with incidence of the at least oneobjective occurrence as depicted in FIG. 6 a. For instance, thecorrelation module 106 of the computing device 10 correlating thesubjective user state data 60 with the objective occurrence data 70*based, at least in part, on a determination by the temporal relationshipdetermination module 239 of whether the at least one subjective userstate occurred before, after, or at least partially concurrently withincidence of the at least one objective occurrence.

In some implementations, the correlation operation 306 may include anoperation 614 for correlating the subjective user state data with theobjective occurrence data based, at least in part, on referencinghistorical data as depicted in FIG. 6 a. For instance, the correlationmodule 106 of the computing device 10 correlating the subjective userstate data 60 with the objective occurrence data 70* based, at least inpart, on referencing by the historical data referencing data 241 ofhistorical data (e.g., population trends such as the superior efficacyof ibuprofen as opposed to acetaminophen in reducing toothaches in thegeneral population, user medical data such as genetic, metabolome, orproteome information, historical sequential patterns particular to theuser 20* or to the overall population such as people having a hangoverafter drinking excessively, and so forth).

In various implementations, operation 614 may include one or moreoperations. For example, in some implementations, operation 614 mayinclude an operation 616 for correlating the subjective user state datawith the objective occurrence data based, at least in part, onhistorical data indicative of a link between a subjective user statetype and an objective occurrence type as depicted in FIG. 6 a. Forinstance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* based, at least in part, on the historical datareferencing module 241 referencing historical data indicative of a linkbetween a subjective user state type and an objective occurrence type(e.g., historical data suggests or indicate a link between a person'smental well-being and exercise).

In some implementations, operation 616 may further include an operation618 for correlating the subjective user state data with the objectiveoccurrence data based, at least in part, on a historical sequentialpattern as depicted in FIG. 6 a. For instance, the correlation module106 of the computing device 10 correlating the subjective user statedata 60 with the objective occurrence data 70* based, at least in part,on the historical data referencing module 241 referencing a historicalsequential pattern (e.g., research indicates that people tend to feelbetter after exercising).

In some implementations, operation 614 may include an operation 620 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on historical medical data of the user asdepicted in FIG. 6 a. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60 withthe objective occurrence data 70* based, at least in part, on thehistorical data referencing module 241 referencing historical medicaldata (e.g., genetic, metabolome, or proteome information or medicalrecords of the user 20* or of others related to, for example, diabetesor heart disease).

In various implementations, the correlation operation 306 of FIG. 3 mayinclude an operation 622 for determining a second sequential patternassociated with at least a second subjective user state indicated by thesubjective user state data and at least a second objective occurrenceindicated by the objective occurrence data as depicted in FIG. 6 b. Forinstance, the sequential pattern determination module 236 of thecomputing device 10 determining a second sequential pattern associatedwith at least a second subjective user state indicated by the subjectiveuser state data 60 and at least a second objective occurrence indicatedby the objective occurrence data 70*.

Operation 622, in some instances, may further include an operation 623for comparing the one sequential pattern to the second sequentialpattern to determine whether the first sequential pattern at leastsubstantially matches the second sequential pattern as depicted in FIG.6 b. For instance, the sequential pattern comparison module 242 (seeFIG. 2 c) of the computing device 10 comparing the one sequentialpattern to the second sequential pattern to determine whether the firstsequential pattern at least substantially matches the second sequentialpattern.

In various alternative implementations, operation 623 may furtherinclude one or more additional operations. For example, in someimplementations, operation 623 may include an operation 624 fordetermining whether the at least one subjective user state is equivalentto the at least a second subjective user state as depicted in FIG. 6 b.For instance, the subjective user state equivalence determination module243 (see FIG. 2 c) of the computing device 10 determining whether the atleast one subjective user state (e.g., backache) is equivalent to the atleast a second subjective user state (e.g., backache).

In some implementations, operation 623 may include an operation 626 fordetermining whether the at least one subjective user state is at leastproximately equivalent in meaning to the at least a second subjectiveuser state as depicted in FIG. 6 b. For instance, the subjective userstate equivalence determination module 243 of the computing device 10determining whether the at least one subjective user state (e.g., angry)is at least proximately equivalent in meaning to the at least a secondsubjective user state (e.g., enraged).

In some implementations, operation 623 may include an operation 628 fordetermining whether the at least one subjective user state isproximately equivalent to the at least a second subjective user state asdepicted in FIG. 6 b. For instance, the subjective user stateequivalence determination module 243 of the computing device 10determining whether the at least one subjective user state (e.g.,slightly drowsy) is proximately equivalent to the at least a secondsubjective user state (e.g., somewhat drowsy).

In some implementations, operation 623 may include an operation 630 fordetermining whether the at least one subjective user state is acontrasting subjective user state from the at least a second subjectiveuser state as depicted in FIG. 6 b. For instance, the subjective userstate contrast determination module 245 (see FIG. 2 c) of the computingdevice 10 determining whether the at least one subjective user state(e.g., extreme pain) is a contrasting subjective user state from the atleast a second subjective user state (e.g., moderate or no pain).

In some implementations, operation 623 may include an operation 632 fordetermining whether the at least one objective occurrence is equivalentto the at least a second objective occurrence as depicted in FIG. 6 b.For instance, the objective occurrence equivalence determination module244 (see FIG. 2 c) of the computing device 10 determining whether the atleast one objective occurrence (e.g., drinking green tea) is equivalentto the at least a second objective occurrence (e.g., drinking greentea).

In some implementations, operation 623 may include an operation 634 fordetermining whether the at least one objective occurrence is at leastproximately equivalent in meaning to the at least a second objectiveoccurrence as depicted in FIG. 6 b. For instance, the objectiveoccurrence equivalence determination module 244 of the computing device10 determining whether the at least one objective occurrence (e.g.,overcast day) is at least proximately equivalent in meaning to the atleast a second objective occurrence (e.g., cloudy day).

In some implementations, operation 623 may include an operation 636 fordetermining whether the at least one objective occurrence is proximatelyequivalent to the at least a second objective occurrence as depicted inFIG. 6 c. For instance, the objective occurrence equivalencedetermination module 244 of the computing device 10 determining whetherthe at least one objective occurrence (e.g., jogging for 30 minutes) isproximately equivalent to the at least a second objective occurrence(e.g., jogging for 25 minutes).

In some implementations, operation 623 may include an operation 638 fordetermining whether the at least one objective occurrence is acontrasting objective occurrence from the at least a second objectiveoccurrence as depicted in FIG. 6 c. For instance, the objectiveoccurrence contrast determination module 246 (see FIG. 2 c) of thecomputing device 10 determining whether the at least one objectiveoccurrence (e.g., jogging for one hour) is a contrasting objectiveoccurrence from the at least a second objective occurrence (e.g.,jogging for thirty minutes or not jogging at all).

In some implementations, operation 623 may include an operation 640 fordetermining whether the at least one subjective user state occurredwithin a predefined time increment from incidence of the at least oneobjective occurrence as depicted in FIG. 6 c. For instance, the “withinpredefined time increment” determination module 238 of the computingdevice 10 determining whether the at least one subjective user state(e.g., upset stomach) occurred within a predefined time increment (e.g.,three hours) from incidence of the at least one objective occurrence(e.g., eating a chocolate sundae).

Operation 640 may, in some instances, include an additional operation642 for determining whether the at least a second subjective user stateoccurred within the predefined time increment from incidence of the atleast a second objective occurrence as depicted in FIG. 6 c. Forinstance, the “within predefined time increment” determination module238 of the computing device 10 determining whether the at least a secondsubjective user state (e.g., another upset stomach) occurred within thepredefined time increment (e.g., three hours) from incidence of the atleast a second objective occurrence (e.g., eating another chocolatesundae).

In various implementations, operation 622 may include an operation 644for determining a first sequential pattern by determining at leastwhether the at least one subjective user state occurred before, after,or at least partially concurrently with incidence of the at least oneobjective occurrence as depicted in FIG. 6 c. For instance, the temporalrelationship determination module 239 of the computing device 10determining a first sequential pattern by determining at least whetherthe at least one subjective user state occurred before, after, or atleast partially concurrently with incidence of the at least oneobjective occurrence.

In some implementations, operation 644 may include an additionaloperation 646 for determining the second sequential pattern bydetermining at least whether the at least a second subjective user stateoccurred before, after, or at least partially concurrently withincidence of the at least a second objective occurrence as depicted inFIG. 6 c. For instance, the temporal relationship determination module239 of the computing device 10 determining the second sequential patternby determining at least whether the at least a second subjective userstate occurred before, after, or at least partially concurrently withincidence of the at least a second objective occurrence.

In various implementations, operation 622 may include an operation 650for determining the one sequential pattern by determining at least anextent of time difference between incidence of the at least onesubjective user state and incidence of the at least one objectiveoccurrence as depicted in FIG. 6 d. For instance, the subjective userstate and objective occurrence time difference determination module 240of the computing device 10 determining the one sequential pattern bydetermining at least an extent of time difference (e.g., one hour)between incidence of the at least one subjective user state (e.g., upsetstomach) and incidence of the at least one objective occurrence (e.g.,consumption of chocolate sundae).

Operation 650 may, in some instances, include an additional operation652 for determining the second sequential pattern by determining atleast an extent of time difference between incidence of the at least asecond subjective user state and incidence of the at least a secondobjective occurrence as depicted in FIG. 6 d. For instance, thesubjective user state and objective occurrence time differencedetermination module 240 of the computing device 10 determining thesecond sequential pattern by determining at least an extent of timedifference (e.g., two hours) between incidence of the at least a secondsubjective user state (e.g., another upset stomach) and incidence of theat least a second objective occurrence (e.g., consumption of anotherchocolate sundae).

In some implementations, the correlation operation 306 of FIG. 3 mayinclude an operation 656 for determining strength of correlation betweenthe subjective user state data and the objective occurrence data asdepicted in FIG. 6 d. For instance, the strength of correlationdetermination module 250 (see FIG. 2 c) of the computing device 10determining strength of correlation between the subjective user statedata 60 and the objective occurrence data 70* based, at least in part,on results provided by the sequential pattern comparison module 242.

In some implementations, the correlation operation 306 may include anoperation 658 for correlating the subjective user state data with theobjective occurrence data at a server as depicted in FIG. 6 d. Forinstance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* when the computing device 10 is a network server.

In some implementations, the correlation operation 306 may include anoperation 660 for correlating the subjective user state data with theobjective occurrence data at a handheld device as depicted in FIG. 6 d.For instance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* when the computing device 10 is a handheld device.

In some implementations, the correlation operation 306 may include anoperation 662 for correlating the subjective user state data with theobjective occurrence data at a peer-to-peer network component device asdepicted in FIG. 6 d. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60 withthe objective occurrence data 70* when the computing device 10 is apeer-to-peer network component device.

Referring back to FIG. 3, the presentation operation 308 may include oneor more additional operations in various alternative embodiments. Forexample, in some implementations, the presentation operation 308 mayinclude a display operation 702 for displaying the one or more resultsvia a user interface as depicted in FIG. 7 a. For instance, the displaymodule 254 (see FIG. 2 d) of the computing device 10 displaying the oneor more results of the correlation via a user interface 122.

In some implementations, the presentation operation 308 may include atransmission operation 704 for transmitting the one or more results viaa network interface as depicted in FIG. 7 a. For instance, thetransmission module 252 (see FIG. 2 d) of the computing device 10transmitting the one or more results of the correlation via a networkinterface 120.

The transmission operation 704 may further include one or moreadditional operations. For example, in some implementations, thetransmission operation 704 may include an operation 706 for transmittingthe one or more results to the user as depicted in FIG. 7 a. Forinstance, the transmission module 252 of the computing device 10transmitting the one or more results of the correlation to the user 20a.

In some implementations, the transmission operation 704 may include anoperation 708 for transmitting the one or more results to one or morethird parties as depicted in FIG. 7 a. For instance, the transmissionmodule 252 of the computing device 10 transmitting the one or moreresults of the correlation to one or more third parties 50.

In some implementations, the presentation operation 308 of FIG. 3 mayinclude an operation 710 for presenting an indication of a sequentialrelationship between the at least one subjective user state and the atleast one objective occurrence as depicted in FIG. 7 a. For instance,the sequential relationship presentation module 256 (see FIG. 2 d) ofthe computing device 10 presenting an indication of a sequentialrelationship between the at least one subjective user state (e.g.,hangover) and the at least one objective occurrence (e.g., drinking fiveshots of whiskey). An example indication might state that the “last timethe user drank five shots of whiskey, the user had a hangover thefollowing morning.”

In some implementations, the presentation operation 308 may include anoperation 714 for presenting a prediction of a future subjective userstate resulting from a future objective occurrence associated with theuser as depicted in FIG. 7 a. For instance, the prediction presentationmodule 258 (see FIG. 2 d) of the computing device 10 presenting aprediction of a future subjective user state resulting from a futureobjective occurrence associated with the user 20*. An example predictionmight state that “if the user drinks five shots of whiskey tonight, theuser will have a hangover tomorrow.”

In some implementations, the presentation operation 308 may include anoperation 716 for presenting a prediction of a future subjective userstate resulting from a past objective occurrence associated with theuser as depicted in FIG. 7 a. For instance, the prediction presentationmodule 258 of the computing device 10 presenting a prediction of afuture subjective user state resulting from a past objective occurrenceassociated with the user 20*. An example prediction might state that“the user will have a hangover tomorrow since the user drank five shotsof whiskey tonight.”

In some implementations, the presentation operation 308 may include anoperation 718 for presenting a past subjective user state in connectionwith a past objective occurrence associated with the user as depicted inFIG. 7 a. For instance, the past presentation module 260 of thecomputing device 10 presenting a past subjective user state inconnection with a past objective occurrence associated with the user20*. An example of such a presentation might state that “the user gotdepressed the last time it rained.”

In some implementations, the presentation operation 308 may include anoperation 720 for presenting a recommendation for a future action asdepicted in FIG. 7 b. For instance, the recommendation module 262 (seeFIG. 2 d) of the computing device 10 presenting a recommendation for afuture action. An example recommendation might state that “the usershould not drink five shots of whiskey.”

Operation 720 may, in some instances, include an additional operation722 for presenting a justification for the recommendation as depicted inFIG. 7 b. For instance, the justification module 264 (see FIG. 2 d) ofthe computing device 10 presenting a justification for therecommendation. An example justification might state that “the usershould not drink five shots of whiskey because the last time the userdrank five shots of whiskey, the user got a hangover.”

In some implementations, the presentation operation 308 may include anoperation 724 for presenting an indication of a strength of correlationbetween the subjective user state data and the objective occurrence dataas depicted in FIG. 7 b. For instance, the strength of correlationpresentation module 266 presenting an indication of a strength ofcorrelation between the subjective user state data 60 and the objectiveoccurrence data 70*.

In some implementations, the presentation operation 308 may include anoperation 726 for presenting one or more results of the correlating inresponse to a reporting of an occurrence of another objective occurrenceassociated with the user as depicted in FIG. 7 b. For instance, thepresentation module 108 of the computing device 10 presenting one ormore results of the correlating in response to a reporting of anoccurrence of another objective occurrence (e.g., drinking one shot ofwhiskey) associated with the user 20*.

In various implementations, operation 726 may further include one ormore additional operations. For example, in some implementations,operation 726 may include an operation 728 for presenting one or moreresults of the correlating in response to a reporting of an eventexecuted by the user as depicted in FIG. 7 b. For instance, thepresentation module 108 of the computing device 10 presenting one ormore results of the correlating in response to a reporting (e.g., viamicroblog) of an event (e.g., visiting a bar) executed by the user 20*.

In some implementations, operation 726 may include an operation 730 forpresenting one or more results of the correlating in response to areporting of an event executed by one or more third parties as depictedin FIG. 7 b. For instance, the presentation module 108 of the computingdevice 10 presenting one or more results of the correlating in responseto a reporting of an event executed by one or more third parties 50(e.g., third party inviting user to bar).

In some implementations, operation 726 may include an operation 732 forpresenting one or more results of the correlating in response to areporting of an occurrence of an external event as depicted in FIG. 7 b.For instance, the presentation module 108 of the computing device 10presenting one or more results of the correlating in response to areporting of an occurrence of an external event (e.g., announcement ofnew bar opening).

In some implementations, the presentation operation 308 of FIG. 3 mayinclude an operation 734 for presenting one or more results of thecorrelating in response to a reporting of an occurrence of anothersubjective user state as depicted in FIG. 7 b. For instance, thepresentation module 108 of the computing device 10 presenting one ormore results of the correlating in response to a reporting of anoccurrence of another subjective user state (e.g., hangover). An examplepresentation might indicate that “the user also had a hangover the lasttime he drank five shots of whiskey.”

In some implementations, the presentation operation 308 may include anoperation 736 for presenting one or more results of the correlating inresponse to an inquiry made by the user as depicted in FIG. 7 b. Forinstance, the presentation module 108 of the computing device 10presenting one or more results of the correlating in response to aninquiry (e.g., why do I have a headache this morning?) made by the user20*.

In some implementations, the presentation operation 308 may include anoperation 738 for presenting one or more results of the correlating inresponse to an inquiry made by a third party as depicted in FIG. 7 b.For instance, the presentation module 108 of the computing device 10presenting one or more results of the correlating in response to aninquiry (e.g., why is the user lethargic?) made by a third party 50.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those having skill in the art will recognize that it is common withinthe art to describe devices and/or processes in the fashion set forthherein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should typically be interpreted to mean at least the recitednumber (e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

In those instances where a convention analogous to “at least one of A,B, or C, etc.” is used, in general such a construction is intended inthe sense one having skill in the art would understand the convention(e.g., “a system having at least one of A, B, or C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

1. A hardware-implemented system comprising: a subjective user statedata acquisition module configured to acquire at least subjective userstate data indicating at least one subjective user state associated witha user; an objective occurrence data acquisition module configured toacquire at least objective occurrence data indicating at least oneobjective occurrence associated with the user; and a correlation moduleconfigured to correlate the acquired subjective user state data with theacquired objective occurrence data by determining at least onesequential pattern associated with the at least one subjective userstate and the at least one objective occurrence.
 2. Thehardware-implemented system of claim 1, wherein said subjective userstate data acquisition module configured to acquire at least subjectiveuser state data indicating at least one subjective user state associatedwith a user comprises: a subjective user state data reception moduleconfigured to receive the subjective user state data.
 3. Thehardware-implemented system of claim 2, wherein said subjective userstate data reception module configured to receive the subjective userstate data comprises: a user interface data reception module configuredto receive the subjective user state data through a user interface. 4.The hardware-implemented system of claim 2, wherein said subjective userstate data reception module configured to receive the subjective userstate data comprises: a network interface data reception moduleconfigured to receive the subjective user state data through a networkinterface.
 5. The hardware-implemented system of claim 4, wherein saidnetwork interface data reception module configured to receive thesubjective user state data through a network interface comprises: anetwork interface data reception module configured to receive thesubjective user state data via one or more blog entries.
 6. Thehardware-implemented system of claim 4, wherein said network interfacedata reception module configured to receive the subjective user statedata through a network interface comprises: a network interface datareception module configured to receive the subjective user state datavia one or more status reports generated by the user.
 7. Thehardware-implemented system of claim 1, wherein said subjective userstate data acquisition module configured to acquire at least subjectiveuser state data indicating at least one subjective user state associatedwith a user comprises: a subjective user state data acquisition moduleconfigured to acquire data indicating one or more subjective mentalstates of the user.
 8. The hardware-implemented system of claim 1,wherein said subjective user state data acquisition module configured toacquire at least subjective user state data indicating at least onesubjective user state associated with a user comprises: a subjectiveuser state data acquisition module configured to acquire data indicatingone or more subjective physical state of the user.
 9. Thehardware-implemented system of claim 1, wherein said subjective userstate data acquisition module configured to acquire at least subjectiveuser state data indicating at least one subjective user state associatedwith a user comprises: a subjective user state data acquisition moduleconfigured to acquire data indicating one or more subjective overallstate of the user.
 10. The hardware-implemented system of claim 1,wherein said subjective user state data acquisition module configured toacquire at least subjective user state data indicating at least onesubjective user state associated with a user comprises: a subjectiveuser state data acquisition module configured to acquire subjective userstate data indicating a second subjective user state associated with theuser.
 11. The hardware-implemented system of claim 1, wherein saidsubjective user state data acquisition module configured to acquire atleast subjective user state data indicating at least one subjective userstate associated with a user comprises: a time stamp acquisition moduleconfigured to acquire one or more time stamps associated with the atleast one subjective user state.
 12. The hardware-implemented system ofclaim 1, wherein said objective occurrence data acquisition moduleconfigured to acquire at least objective occurrence data indicating atleast one objective occurrence associated with the user comprises: anobjective occurrence data reception module configured to receive theobjective occurrence data.
 13. The hardware-implemented system of claim12, wherein said objective occurrence data reception module configuredto receive the objective occurrence data comprises: an objectiveoccurrence data reception module configured to receive the objectiveoccurrence data in one or more blog entries.
 14. Thehardware-implemented system of claim 12, wherein said objectiveoccurrence data reception module configured to receive the objectiveoccurrence data comprises: an objective occurrence data reception moduleconfigured to receive the objective occurrence data in one or morestatus reports.
 15. The hardware-implemented system of claim 1, whereinsaid objective occurrence data acquisition module configured to acquireat least objective occurrence data indicating at least one objectiveoccurrence associated with the user comprises: an objective occurrencedata acquisition module configured to acquire objective occurrence dataindicating a second objective occurrence associated with the user. 16.The hardware-implemented system of claim 1, wherein said objectiveoccurrence data acquisition module configured to acquire at leastobjective occurrence data indicating at least one objective occurrenceassociated with the user comprises: a time stamp acquisition moduleconfigured to acquire one or more time stamps associated with the atleast one objective occurrence.
 17. The hardware-implemented system ofclaim 1, wherein said objective occurrence data acquisition moduleconfigured to acquire at least objective occurrence data indicating atleast one objective occurrence associated with the user comprises: anobjective occurrence data acquisition module configured to acquireobjective occurrence data indicating one or more objective occurrencesof one or more ingestions of one or more medicines.
 18. Thehardware-implemented system of claim 1, wherein said objectiveoccurrence data acquisition module configured to acquire at leastobjective occurrence data indicating at least one objective occurrenceassociated with the user comprises: an objective occurrence dataacquisition module configured to acquire objective occurrence dataindicating one or more objective occurrences of one or more ingestionsof one or more food items.
 19. The hardware-implemented system of claim1, wherein said objective occurrence data acquisition module configuredto acquire at least objective occurrence data indicating at least oneobjective occurrence associated with the user comprises: an objectiveoccurrence data acquisition module configured to acquire objectiveoccurrence data indicating one or more objective occurrences of one ormore ingestions of one or more nutraceuticals.
 20. Thehardware-implemented system of claim 1, wherein said objectiveoccurrence data acquisition module configured to acquire at leastobjective occurrence data indicating at least one objective occurrenceassociated with the user comprises: an objective occurrence dataacquisition module configured to acquire objective occurrence dataindicating one or more objective occurrences of one or more physicalcharacteristics of the user.
 21. The hardware-implemented system ofclaim 1, wherein said objective occurrence data acquisition moduleconfigured to acquire at least objective occurrence data indicating atleast one objective occurrence associated with the user comprises: anobjective occurrence data acquisition module configured to acquireobjective occurrence data indicating one or more objective occurrencesof one or more resting, learning, or recreational activities by theuser.
 22. The hardware-implemented system of claim 1, wherein saidobjective occurrence data acquisition module configured to acquire atleast objective occurrence data indicating at least one objectiveoccurrence associated with the user comprises: an objective occurrencedata acquisition module configured to acquire objective occurrence dataindicating one or more objective occurrences of one or more externalevents.
 23. The hardware-implemented system of claim 1, wherein saidobjective occurrence data acquisition module configured to acquire atleast objective occurrence data indicating at least one objectiveoccurrence associated with the user comprises: an objective occurrencedata acquisition module configured to acquire objective occurrence dataindicating one or more objective occurrences related to one or morelocations of the user.
 24. The hardware-implemented system of claim 1,wherein said correlation module configured to correlate the acquiredsubjective user state data with the acquired objective occurrence databy determining at least one sequential pattern associated with the atleast one subjective user state and the at least one objectiveoccurrence comprises: a sequential pattern determination moduleconfigured to determine one or more sequential patterns associated withthe at least one subjective user state and the at least one objectiveoccurrence.
 25. The hardware-implemented system of claim 24, whereinsaid sequential pattern determination module configured to determine oneor more sequential patterns associated with the at least one subjectiveuser state and the at least one objective occurrence comprises: a withinpredefined time increment determination module configured to determineat least whether the one subjective user state occurred within apredefined time increment from incidence of the one objectiveoccurrence.
 26. The hardware-implemented system of claim 24, whereinsaid sequential pattern determination module configured to determine oneor more sequential patterns associated with the at least one subjectiveuser state and the at least one objective occurrence comprises: atemporal relationship determination module configured to determine atleast whether the one subjective user state occurred before, after, orat least partially concurrently with incidence of the one objectiveoccurrence.
 27. The hardware-implemented system of claim 24, whereinsaid sequential pattern determination module configured to determine oneor more sequential patterns associated with the at least one subjectiveuser state and the at least one objective occurrence comprises: ahistorical data referencing module configured to reference historicaldata.
 28. The hardware-implemented system of claim 27, wherein saidhistorical data referencing module configured to reference historicaldata comprises: a historical data referencing module configured toreference historical data indicative of at least a link between asubjective user state type and an objective occurrence type.
 29. Thehardware-implemented system of claim 27, wherein said historical datareferencing module configured to reference historical data comprises: ahistorical data referencing module configured to reference historicaldata indicating at least one historical sequential pattern.
 30. Thehardware-implemented system of claim 27, wherein said historical datareferencing module configured to reference historical data comprises: ahistorical data referencing module configured to reference historicalmedical data of the user.
 31. The hardware-implemented system of claim1, wherein said correlation module configured to correlate the acquiredsubjective user state data with the acquired objective occurrence databy determining at least one sequential pattern associated with the atleast one subjective user state and the at least one objectiveoccurrence comprises: a sequential pattern comparison module configuredto compare the one sequential pattern to a second sequential pattern todetermine whether the one sequential pattern at least substantiallymatches the second sequential pattern.
 32. The hardware-implementedsystem of claim 31, wherein said sequential pattern comparison moduleconfigured to compare the one sequential pattern to a second sequentialpattern to determine whether the one sequential pattern at leastsubstantially matches the second sequential pattern comprises: asubjective user state equivalence determination module configured todetermine whether the one subjective user state is equivalent to asecond subjective user state indicated by the subjective user statedata.
 33. The hardware-implemented system of claim 31, wherein saidsequential pattern comparison module configured to compare the onesequential pattern to a second sequential pattern to determine whetherthe one sequential pattern at least substantially matches the secondsequential pattern comprises: a subjective user state equivalencedetermination module configured to determine whether the one subjectiveuser state is at least proximately equivalent in meaning to a secondsubjective user state indicated by the subjective user state data. 34.The hardware-implemented system of claim 31, wherein said sequentialpattern comparison module configured to compare the one sequentialpattern to a second sequential pattern to determine whether the onesequential pattern at least substantially matches the second sequentialpattern comprises: a subjective user state equivalence determinationmodule configured to determine whether the one subjective user state isproximately equivalent to a second subjective user state indicated bythe subjective user state data.
 35. The hardware-implemented system ofclaim 31, wherein said sequential pattern comparison module configuredto compare the one sequential pattern to a second sequential pattern todetermine whether the one sequential pattern at least substantiallymatches the second sequential pattern comprises: a subjective user statecontrast determination module configured to determine whether the onesubjective user state is a contrasting subjective user state from asecond subjective user state indicated by the subjective user statedata.
 36. The hardware-implemented system of claim 31, wherein saidsequential pattern comparison module configured to compare the onesequential pattern to a second sequential pattern to determine whetherthe one sequential pattern at least substantially matches the secondsequential pattern comprises: an objective occurrence equivalencedetermination module configured to determine whether the one objectiveoccurrence is equivalent to a second objective occurrence indicated bythe objective occurrence data.
 37. The hardware-implemented system ofclaim 31, wherein said sequential pattern comparison module configuredto compare the one sequential pattern to a second sequential pattern todetermine whether the one sequential pattern at least substantiallymatches the second sequential pattern comprises: an objective occurrenceequivalence determination module configured to determine whether the oneobjective occurrence is at least proximately equivalent in meaning to asecond objective occurrence indicated by the objective occurrence data.38. The hardware-implemented system of claim 31, wherein said sequentialpattern comparison module configured to compare the one sequentialpattern to a second sequential pattern to determine whether the onesequential pattern at least substantially matches the second sequentialpattern comprises: an objective occurrence equivalence determinationmodule configured to determine whether the one objective occurrence isproximately equivalent to a second objective occurrence indicated by theobjective occurrence data.
 39. The hardware-implemented system of claim31, wherein said sequential pattern comparison module configured tocompare the one sequential pattern to a second sequential pattern todetermine whether the one sequential pattern at least substantiallymatches the second sequential pattern comprises: an objective occurrencecontrast determination module configured to determine whether the oneobjective occurrence is a contrasting objective occurrence to a secondobjective occurrence indicated by the objective occurrence data.
 40. Thehardware-implemented system of claim 31, wherein said sequential patterncomparison module configured to compare the one sequential pattern to asecond sequential pattern to determine whether the one sequentialpattern at least substantially matches the second sequential patterncomprises: a temporal relationship comparison module configured tocompare a first temporal relationship between the one subjective userstate and the one objective occurrence associated with the onesequential pattern to a second temporal relationship between a secondsubjective user state and a second objective occurrence associated withthe second sequential pattern.
 41. A method comprising: acquiring atleast subjective user state data indicating at least one subjective userstate associated with a user; acquiring at least objective occurrencedata indicating at least one objective occurrence associated with theuser; and correlating, by a hardware computing device, the acquiredsubjective user state data with the acquired objective occurrence databy determining at least one sequential pattern associated with the atleast one subjective user state and the at least one objectiveoccurrence.